{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Predicting PG&E Feeder Level Congestion; Cui, Eichenlaub, Healy, Tikson\n", "| Student | Contribution|\n", "|-----------------|-----------------|\n", "| Cui, Xiaoxi | CIMIS weather data sourcing, EDA, cleaning, and merging into data pipeline. Prediciton Problem #2 |\n", "| Eichenlaub, Brooke | Lead Data Pipeline Architect, final data frame EDA, Project background author, project objectives author |\n", "| Healy, Phillip | Prediction Problem #1, Lead author of abstract, RA and Conclusion section, EDA on CALMAC Load Shapes, some EDA on main dataframe |\n", "| Tikson, Parker | EV Data sourcing, EDA, cleaning, and merging into data pipeline, Prediction Problem #3|\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Abstract (5 points)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "California’s distribution grid is facing increasing strain as electrification, rooftop solar, and new electric end uses grow faster than historical planning assumptions. Much of this stress appears at the feeder level, where thermal and hosting-capacity limits can restrict additional DER interconnections and slow progress toward statewide climate goals. Existing tools such as PG&E’s Integration Capacity Analysis (ICA) maps provide only static snapshots and are costly to update, leaving planners with limited ability to anticipate when and where future constraints may emerge. This creates a need for lightweight forecasting methods that can complement engineering studies and support short- and medium-term resource allocation.\n", "\n", "To help fill this gap, we built a set of predictive models using publicly available data, including CALMAC load shapes, CIMIS weather records, feeder attributes, and indicators of DER and EV adoption. We studied three related questions: predicting continuous feeder headroom by month and hour, classifying whether headroom will fall below 1 MW, and assigning each observation to a low-, medium-, or high-risk congestion tier. We tested linear models, regularized models, decision trees, and ensemble methods using an 80/20 train–test split and cross-validation.\n", "Across all three prediction problems, Random Forest models performed far better than linear approaches. For the regression task, the Random Forest reached an RMSE of about 240 kW with an R² near 0.99, compared with errors over 1.5 MW for OLS, Ridge, and Lasso. The classification tasks showed similar improvements, with the Random Forest capturing important non-linear patterns in customer mix, distributed generation, and time-of-day behavior.\n", "These results suggest that lightweight machine-learning models can provide meaningful early signals of feeder stress. This approach can help utilities prioritize upgrades, plan DER programs, and evaluate future grid scenarios with greater confidence.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Project Background (5 points)\n", "California’s electric grid is undergoing a rapid transformation driven by electricity demand––which is growing for the first time in over a decade––and the accelerated adoption of distributed energy resources (DERs) along with electric products such as electric vehicles (EVs) and heat-pumps. Much of this growth is occurring within the distribution system, which was historically designed for one way power flows and predictable load growth. Rapid load growth due to electrification, paired with two-way power flow systems which export energy in addition to importing it, are key contributors to today’s distribution-grid congestion. This poses a serious challenge for California’s electrification goals and prevents utilities from interconnecting DERs at an acceptable pace. \n", "\n", "Export-driven congestion (i.e. ‘generation congestion’) is especially salient in the Pacific Gas & Electric (PG&E) territory, where rooftop solar and community scale solar projects have been growing quickly. On certain feeders, mid-day exports from large numbers of solar customers already push circuits toward their hosting-capacity limits. When a feeder approaches its generation headroom constraints, PG&E may be unable to interconnect additional solar customers without costly upgrades. Moreover, mid-day generation congestion can lead to solar project curtailment, resulting in wasted energy. \n", "\n", "The primary tool that stakeholders use to understand these constraints today is the Integration Capacity Analysis (ICA) map. ICA provides a snapshot of generation hosting capacity, but it has some limitations. First, ICA represents only current conditions, and is not useful for identifying which where constraints may emerge in future years under growing DER adoption. Second, generating ICA values required PG&E to run computationally intensive engineering models that have little scalability. This makes it difficult for utilities or regulators to re-run ICA projections to produce iterative scenario-based estimates. These limitations create a gap for decision-makers , who lack the ability to forecast where hosting-capacity constraints are likely to emerge. Regulators and program administrators at organizations like Community Choice Aggregators and the California Energy commission implement various programs meant to improve DER adoption and grid health–such as behind-the-meter battery storage programs and load shifting programs, yet maximizing the value of these programs depends on understanding where the grid is most vulnerable.\n", "\n", "In this context, a simple and computationally efficient predictive model of feeder-level generation congestion could provide meaningful value to PG&E, regulators, and program administrators. By using readily available inputs, such as load shapes, climate zone characteristics, and EV/DER adoption patterns, such a model can approximate where peak generation is likely to exceed feeder capabilities under future or varied scenarios. While a simple predictive model could not replace PG&E’s internal engineering studies, it can serve as a complementary tool to identify feeders which may be under stress in future scenarios, using forecasted feature data. The model outputs could be used to strategically target load-shifting and battery storage programs, which help the distribution grid unlock more capacity without making physical infrastructure upgrades. \n", "\n", "California’s experience with flexible load programs emphasizes this need. Statewide programs, like managed EV charging pilots and the Demand Side Grid Support program, have historically struggled with locational targeting. In many cases, programs have underperformed, in part, because enrollment was not targeted to feeders with the most need. A predictive congestion model that identifies feeders likely to face future stress could therefore enable more impactful and cost-effective deployment of these programs. \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Project Objective (5 points)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The objective of this project is to develop and evaluate machine-learning models that forecast feeder-level congestion (measured as remaining hosting capacity in MW of head room) across PG&E’s distribution system. Using publicly available data sources, including ICA hosting-capacity values, CIMIS weather data, EV and DER adoption indicators, and aggregated CALMAC load-shape features, we construct models capable of predicting both continuous headroom and categorical congestion risk at the feeder and hour level. The purpose of these models is to create a lightweight, frequently updateable forecasting tool that complements PG&E’s ICA maps, which require detailed engineering planning to recompute and are updated only every three months.\n", "\n", "Understanding feeder headroom is important because distribution-grid congestion is a major bottleneck to California’s electrification goals. Utilities and regulators need timely and granular indicators of where remaining capacity is shrinking so they can more effectively allocate resources, such as managed charging programs, DER incentives, operational equipment adjustments, or targeted upgrades. Our forecasting framework enables earlier identification of emerging constraints, faster scenario analysis, and more informed planning under increasing load from EVs, heat pumps, and solar adoption.\n", "To meet these objectives, we investigate three forecasting problems:\n", "\n", "Continuous Forecasting (Regression):\n", "How much remaining capacity (MW) will a feeder have at hour h of month m?\n", "This supports short-term (0 - 12 month) planning by providing quantitative headroom estimates.\n", "\n", "\n", "Binary Congestion Classification:\n", "Is a feeder likely to fall below 1 MW of headroom at a given hour?\n", "This framing identifies high-risk near-term overload conditions, guiding operational decisions such as deploying inspections, sensors, or temporary curtailments.\n", "\n", "\n", "Multi-Category Congestion Risk Tiering:\n", "What is the congestion risk level (low, medium, or high) during hour h of month m?\n", "The model categorizes feeders into actionable risk tiers (e.g., >5 MW, 1 - 5 MW, <1 MW), enabling medium-term (6 - 36 month) planning such as capital upgrades or non-wires alternatives. \n", "\n", "Novelty:\n", "While PG&E’s ICA tool provides high-fidelity engineering estimates, it is updated infrequently due to the labor- and computational-intensive cost of power-flow modeling. Our approach offers a more dynamic alternative that can be updated weekly or daily at minimal computational cost. By training on 2024 historical data and incorporating forecastable variables (weather, DER growth, load-shape seasonality), the resulting model can be used for scenario analysis, producing potential future insights into feeder-level congestion over the next 1 - 2 years.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Input Data Description (5 points)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Weather Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Weather (Solar radiation / Wind speed / Air temperature): \n", "\n", "The weather data used in this study were obtained from the California Irrigation Management Information System (CIMIS) website (https://cimis.water.ca.gov/), which is operated by the California Department of Water Resources (DWR). CIMIS manages a statewide network of over 145 automated weather stations and was developed in 1982 by DWR in collaboration with the University of California, Davis (UC Davis). The primary purpose of CIMIS is to support efficient irrigation and water resource management; however, the meteorological variables it provides are also widely used for environmental and air quality research.\n", "\n", "For this project, we downloaded hourly weather data including: Solar radiation, Wind speed and Air temperature\n", "\n", "The data were accessed through the CIMIS web portal after logging into the system. Users can select specific stations, time ranges, and sensors before downloading the data in CSV format. To reduce download time and ensure only relevant information was collected, we selected only the required sensors for each station. Because the website allows downloading data for a maximum of five stations at a time, multiple CSV files were downloaded and then aggregated into a single dataset for further analysis.\n", "\n", "To present the original dataset downloaded from CIMIS, we used one-sixth of the full dataset as a representative sample because the complete file exceeds 100 MB in size:\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Stn IdStn NameCIMIS RegionDateHour (PST)JulSol Rad (Ly/day)qcAir Temp (F)qcWind Speed (mph)qc
12FivePointsSan Joaquin Valley1/1/20240100001047.52.6
22FivePointsSan Joaquin Valley1/1/20240200001046.73.3
32FivePointsSan Joaquin Valley1/1/20240300001046.94.3
42FivePointsSan Joaquin Valley1/1/20240400001045.52.6
52FivePointsSan Joaquin Valley1/1/20240500001044.72.4
62FivePointsSan Joaquin Valley1/1/20240600001045.12.4
72FivePointsSan Joaquin Valley1/1/20240700001144.12.6
82FivePointsSan Joaquin Valley1/1/202408000014843.82.8
92FivePointsSan Joaquin Valley1/1/202409000019247.07.1
102FivePointsSan Joaquin Valley1/1/2024100000122549.49.5
\n", "
" ], "text/plain": [ " Stn Id Stn Name CIMIS Region Date Hour (PST) Jul \\\n", "1 2 FivePoints San Joaquin Valley 1/1/2024 0100 001 \n", "2 2 FivePoints San Joaquin Valley 1/1/2024 0200 001 \n", "3 2 FivePoints San Joaquin Valley 1/1/2024 0300 001 \n", "4 2 FivePoints San Joaquin Valley 1/1/2024 0400 001 \n", "5 2 FivePoints San Joaquin Valley 1/1/2024 0500 001 \n", "6 2 FivePoints San Joaquin Valley 1/1/2024 0600 001 \n", "7 2 FivePoints San Joaquin Valley 1/1/2024 0700 001 \n", "8 2 FivePoints San Joaquin Valley 1/1/2024 0800 001 \n", "9 2 FivePoints San Joaquin Valley 1/1/2024 0900 001 \n", "10 2 FivePoints San Joaquin Valley 1/1/2024 1000 001 \n", "\n", " Sol Rad (Ly/day) qc Air Temp (F) qc Wind Speed (mph) qc \n", "1 0 47.5 2.6 \n", "2 0 46.7 3.3 \n", "3 0 46.9 4.3 \n", "4 0 45.5 2.6 \n", "5 0 44.7 2.4 \n", "6 0 45.1 2.4 \n", "7 1 44.1 2.6 \n", "8 48 43.8 2.8 \n", "9 92 47.0 7.1 \n", "10 225 49.4 9.5 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "raw = pd.read_csv(\n", " \"weather/cimis_raw_1.csv\",\n", " skiprows=1, # skip the \"# ---------\" line\n", " engine=\"python\"\n", ")\n", "\n", "# First row (row 0) is the header line as a single string\n", "header = raw.iloc[0, 0].split(\",\")\n", "\n", "# Remaining rows are the data, each as a single comma-separated string\n", "data = raw.iloc[1:, 0].str.split(\",\", expand=True)\n", "\n", "# Assign column names\n", "data.columns = header\n", "\n", "# clean DataFrame\n", "df = data\n", "\n", "df.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Structure:\n", "\n", "The dataset is organized in a tabular format where each row represents one weather observation and each column represents a specific variable. The columns include:\n", "\n", "-Stn Id (station identifier)
\n", "-Stn Name (station name)
\n", "-CIMIS Region (geographic region)
\n", "-Date (calendar date of observation)
\n", "-Hour (PST) (hourly timestamp in Pacific Standard Time)
\n", "-Jul (Julian day indicator)
\n", "-Sol Rad (Ly/day) (solar radiation)
\n", "-Air Temp (F) (air temperature in Fahrenheit)
\n", "-Wind Speed (mph) (wind speed in miles per hour)
\n", "-Several qc columns that represent data quality control flags\n", "\n", "For example, in the first row, Station 2 (FivePoints) recorded weather conditions on 1/1/2024 at 01:00 PST with an air temperature of 47.5°F and wind speed of 2.6 mph." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Granularity / Scope / Temporality:\n", "\n", "The granularity of the dataset is hourly, station-level measurements (which include all active stations in California) from 1/1/2024 - 12/31/2024.\n", "Each row corresponds to:\n", "\n", "-One specific station (e.g., FivePoints)
\n", "-One specific date
\n", "-One specific hour\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Stn IdStn NameCIMIS RegionDateHour (PST)JulSol Rad (Ly/day)qcAir Temp (F)qcWind Speed (mph)qc
25802FivePointsSan Joaquin Valley4/17/202412001081222S3.6
64742FivePointsSan Joaquin Valley9/26/20241800270MMM
108905ShafterSan Joaquin Valley3/28/20241800088MMM
124045ShafterSan Joaquin Valley5/30/20242000151MMM
135555ShafterSan Joaquin Valley7/17/20241900199MMM
.......................................
287562113King City-Oasis Rd.Monterey Bay9/26/20241800270MMM
288187113King City-Oasis Rd.Monterey Bay10/22/20241900296MMM
293492114Arroyo SecoMonterey Bay5/30/20242000151MMM
296346114Arroyo SecoMonterey Bay9/26/20241800270MMM
296971114Arroyo SecoMonterey Bay10/22/20241900296MMM
\n", "

599 rows × 12 columns

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" ], "text/plain": [ " Stn Id Stn Name CIMIS Region Date Hour (PST) \\\n", "2580 2 FivePoints San Joaquin Valley 4/17/2024 1200 \n", "6474 2 FivePoints San Joaquin Valley 9/26/2024 1800 \n", "10890 5 Shafter San Joaquin Valley 3/28/2024 1800 \n", "12404 5 Shafter San Joaquin Valley 5/30/2024 2000 \n", "13555 5 Shafter San Joaquin Valley 7/17/2024 1900 \n", "... ... ... ... ... ... \n", "287562 113 King City-Oasis Rd. Monterey Bay 9/26/2024 1800 \n", "288187 113 King City-Oasis Rd. Monterey Bay 10/22/2024 1900 \n", "293492 114 Arroyo Seco Monterey Bay 5/30/2024 2000 \n", "296346 114 Arroyo Seco Monterey Bay 9/26/2024 1800 \n", "296971 114 Arroyo Seco Monterey Bay 10/22/2024 1900 \n", "\n", " Jul Sol Rad (Ly/day) qc Air Temp (F) qc Wind Speed (mph) qc \n", "2580 108 1222 S 3.6 \n", "6474 270 M M M \n", "10890 088 M M M \n", "12404 151 M M M \n", "13555 199 M M M \n", "... ... ... .. ... .. ... .. \n", "287562 270 M M M \n", "288187 296 M M M \n", "293492 151 M M M \n", "296346 270 M M M \n", "296971 296 M M M \n", "\n", "[599 rows x 12 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\n", " (df['Sol Rad (Ly/day)'] == \"\") |\n", " (df['Wind Speed (mph)'] == \"\") |\n", " (df['Air Temp (F)'] == \"\")\n", "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Faithfulness:\n", "\n", "The data originate from the California Irrigation Management Information System (CIMIS), a government-operated and scientifically maintained network of automated weather stations. This makes the dataset highly reliable and faithful to real-world conditions.\n", "\n", "Also, the presence of qc (quality control) columns indicates that the data undergo validation, increasing trustworthiness.\n", "\n", "As expected for sensor-based data collected over long time periods, the dataset contains explicitly flagged missing values, denoted by the code “M” in the Solar Radiation, Air Temperature, and Wind Speed columns. These missing values occur across multiple stations and dates, indicating that the gaps are likely due to system-wide outages, sensor downtime, or data validation failures." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##. CALMAC Load Shape Data:\n", "The CALMAC load shape dataset used in this project originates from the California Measurement Advisory Council (CALMAC) in partnership with PG&E. The load shape datasets used in this project are Granular Profiles (GPs) produced by PG&E and the analytics firm Demand Side Analystics (DSA) using aggregated smart-meter data from non-participating customers. To create these profiles, PG&E groups customers with similar characteristics into predefined segments and averages their hourly electricity consumption. This produces representative load-shape time series that reflect real consumption patterns across PG&E’s service territory.\n", "Because GPs are aggregated and do not contain customer-level data, they satisfy California privacy requirements and can be shared publicly. Their aggregation makes them useful for analysis while avoiding the confidentiality risks associated with releasing individual Advanced Metering Infrastructure(AMI) records (Source: https://www.calmac.org/granular_profiles.asp).\n", "The specific files used in this project include:\n", "CALMAC_Res_GP_Elec_2024.csv: hourly electric granular profiles\n", "\n", "\n", "CALMAC_Residential_Customer_Electric_Characteristics.csv (multiple files): segmentation attributes\n", "\n", "\n", "CALMAC_Res_Elec_GP_Segments_and_Counts.csv: participant counts per segment\n", "\n", "\n", "CALMAC_Res_Elec_Segment_Cutoffs_Shape.csv and CALMAC_Res_Elec_Segment_Cutoffs_Size.csv: segmentation cutpoints\n", "\n", "\n", "Documentation file CALMAC_GP_Overview.docx: describes methodology\n", "\n", "\n", "\n", "### Structure\n", "\n", "The CALMAC Residential Electric Load Shape dataset consists of 160 standardized daily load shapes (“GP” profiles) representing typical residential electricity consumption patterns across the PG&E service territory. Each GP corresponds to a single customer segment defined by:\n", "- Climate zone (CEC Building Climate Zones or PG&E load research zones)\n", "- Household characteristics (e.g., electric heating, gas heating, AC saturation, home size categories)\n", "- End-use mix (weights of appliances contributing to hourly load)\n", "\n", "Each GP typically represents an average profile derived from load research and advanced metering infrastructure (AMI) samples. The structure is has one row per GP × date × hour.\n", "\n", "### Granularity / Scope / Temporality\n", "\n", "Granularity\n", "- Unit of observation: Hourly electricity consumption\n", "- Segments: GPs represent populations of similar customers, not individuals\n", "- Geography: Not ZIP-specific; instead, each GP belongs to a climate-zone group, which is later mapped to ZIP codes through spatial overlay.\n", "\n", "Scope\n", "- Total residential GPs: 160\n", "- Daily resolution: Each GP provides a full 24-hour load shape\n", "- Monthly resolution: When aggregated, load becomes gp × month × hour, giving:\n", "- 160 GPs\n", "- 12 months\n", "- 24 hours\n", "- ≈ 46,080 month-hour rows\n", "\n", "Temporality\n", "- While CALMAC releases new versions of their data throughout the year, load shapes do not represent yearly data. They are instead a representative aggregate of load, creating from historical load data. \n", "Observation are at the hour level\n", "\n", "Thus, CALMAC provides a static but seasonally varying baseline load shape used widely in utility planning, DR studies, and rate modeling.\n", "\n", "Faithfulness Assessment (Research-Based)\n", "\n", "CALMAC load shapes are considered highly faithful because:\n", "1.\tThe load research methodology is audited and used in regulatory filings (e.g., CPUC GRCs, DR proceedings).\n", "2.\tRepresentative customer segments (GPs) aggregate many customers, smoothing random noise while preserving real differences between customer classes and climates\n", "3.\tSeasonal fidelity - Monthly restructuring preserves realistic differences between summer/winter loads, AC usage, heating usage, etc.\n", "4.\tCALMAC profiles contain no missing hours (except on daylight savings time days), Hour values always span 0–23, and dates are valid.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of GP–date combinations with missing hours: 160\n" ] }, { "data": { "text/html": [ "
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gpdateunique_hours
691_1_NS_C2024-03-1023
4351_1_NS_I2024-03-1023
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gpdatehourkwh
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" ], "text/plain": [ "Empty DataFrame\n", "Columns: [gp, date, hour, kwh]\n", "Index: []" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "load_data = pd.read_csv(\"CALMAC/Res_GP_Elec_2024.csv\")\n", "\n", "# Count hours per (gp, date)\n", "hours_per_day = (\n", " load_data.groupby([\"gp\", \"date\"])[\"hour\"]\n", " .nunique()\n", " .reset_index(name=\"unique_hours\")\n", ")\n", "\n", "# Check for rows where the hour count is not 24\n", "missing_hours = hours_per_day[hours_per_day[\"unique_hours\"] != 24]\n", "\n", "print(\"Number of GP–date combinations with missing hours:\", len(missing_hours))\n", "display(missing_hours.head())\n", "\n", "print(\"Unique hour values:\", sorted(load_data[\"hour\"].unique()))\n", "\n", "# Check for any invalid hour values\n", "invalid_hours = load_data[~load_data[\"hour\"].between(0, 23)]\n", "print(\"Rows with invalid hour values:\", len(invalid_hours))\n", "invalid_hours.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## EV Data Description\n", "Our EV adoption data come from the California Energy Commission’s Zero Emission Vehicle and Infrastructure Statistics Dashboard (https://www.energy.ca.gov/zevstats, Vehicle_Population_Last_updated_04-30-2025_ada.xlsx), which compiles annual DMV registration data for light-duty vehicles across California. We accessed the “Vehicle Population by ZIP Code” dataset, which reports the number of light duty vehicles registered in each ZIP code by vehicle type. The CEC updates these data annually to reflect registrations from the prior calendar year. The underlying CSVs are freely downloadable from the dashboard.\n", "\n", "### SGSTF Summary:\n", "The dataset is an excel file with one row per ZIP code per year. It offers moderate granularity—ZIP-level detail provides regional EV trends but is coarser than feeder boundaries. Regarding electric vehicles, its scope includes both BEVs and PHEVs, which we sum to represent total EV adoption relevant for load-growth modeling. The data are annual, so we treat EV counts and growth as slow-moving structural features rather than time-varying signals. Faithfulness is high given its DMV origin, though registrations reflect home addresses rather than actual charging locations.\n", "We initially also included public and residential charger data from the same source (different dataset) , but chose to exclude it from our feature set due to strong collinearity with EV population counts. We believed including both variables would inflate their apparent influence and reduce interpretability and had already created a second EV metric (“YoY Change”). \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Integration Capacity Analysis Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Origins of Integration Capacity Analysis Data\n", "The Integration Capacity Analysis (ICA) data come from Pacific Gas and Electric Company’s (PG&E) GRIP hosting capacity portal: https://grip.pge.com/\n", "PG&E publishes hosting capacity results as part of its obligations under the CPUC’s Distribution Resource Planning (DRP) proceeding. These data provide generation hosting capacity values per feeder line-section and month-hour \n", "To obtain feeder hosting capacity data:\n", "1. Navigate to PG&E’s ICA hosting capacity dataset portal\n", "2. Download all Division ZIP files, each representing a PG&E operating division\n", "(e.g., North Valley.zip, San Francisco.zip, Peninsula.zip, etc.)\n", "3. Each division ZIP contains hundreds of feeder-level ZIP archives.\n", "\n", "Each feeder ZIP contains a single CSV with hourly ICA values for all line sections on that feeder.\n", "1. The origins of your data. Where did you get the data? How were the data collected from the original sources? You must provide enough information for the reader to be able to access the data themselves. \n", "2. The structure, granularity, scope, temporality and faithfulness (SGSTF) of your data. To discuss these attributes you should load the data into one or more data frames (so you'll start building code cells for the first time). At a minimum, use some basic methods (`.head`, `.loc`, and so on) to provide support for the descriptions you provide for SGSTF. \n", "3. You should also describe which data fields you will use as your target variables, and which you will use as features. \n", "\n", "To obtain feeder line shapefiles:\n", "1. Navigate to PG&E’s ICA hosting capacity dataset portal\n", "2. Download 'GRIP Shape' files\n", "\n", "To obtain feeder charachteristics/metadata:\n", "1. Navigate to PG&E’s ICA hosting capacity dataset portal\n", "2. Click \"system\" on left side pane\n", "3. Click on the three dots next to \"Distribution lines\" under \"system\" menu\n", "4. Click \"add to table\"\n", "5. Download\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ICA Data Structure, Granularity, Scope, Temporality\n", "The ICA line section dataset is structured as one CSV per feeder, where each CSV contains rows for every line section on that feeder. Each row corresponds to a unique combination of:\n", "- month (1–12)\n", "- hour (0–23)\n", "- loading_scenario (e.g., 10th, and 90th percentile case)\n", "- loadorgen (“G” for generation, “L” for load)\n", "- ICA limit values, such as hourly_ica_sg or hourly_ica_of\n", "- Line Section ID\n", "\n", "### Temporality\n", "The ICA data is reported by month hour, at the 90th and 10th pctl. This means each row represents the 90th/10th percentile conditions more that line section during a particular month hour, aggregated accross\n", "all days in that month\n", "\n", "### Granularity\n", "The ICA data is bery granular, with one row per line section. Each feeder can have hundreds of line sections. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import zipfile, glob\n", "import pandas as pd\n", "from io import BytesIO\n", "\n", "division_zip = \"ica_data/raw_zips/Yosemite.zip\"\n", "\n", "with zipfile.ZipFile(division_zip, \"r\") as div_zip:\n", " inner = [f for f in div_zip.namelist() if f.endswith(\".zip\")]\n", " raw_bytes = div_zip.read(inner[0])\n", "\n", " with zipfile.ZipFile(BytesIO(raw_bytes), \"r\") as feeder_zip:\n", " csv_name = [f for f in feeder_zip.namelist() if f.endswith(\".csv\")][0]\n", " df_raw = pd.read_csv(feeder_zip.open(csv_name))\n", "\n", "print(df_raw.head(4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Cleaning (10 points)\n", "In this section you will walk through the data cleaning and merging process. Explain how you make decisions to clean and merge the data. Explain how you convince yourself that the data don't contain problems that will limit your ability to produce a meaningful analysis from them. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Basic Data Aggregation Principles:\n", "\n", "Our data were derived from multiple sources that varied widely in both granularity and scope. To ensure consistency with our prediction objective, we defined our temporal scope as month–hour intervals from January 1, 2024, to December 31, 2024, and our geographic scope as the PG&E service territory. Accordingly, all datasets were aggregated to the month–hour level where necessary. Some datasets were already available at this resolution and required no transformation, while EV data were only available at the annual level and were therefore repeated across all month-hours within each zip code.\n", "\n", "At the geographic level, our target variable and several predictors from the ICA dataset were available at the feeder level, while other datasets were provided at the ZIP code or station level (e.g., CIMIS weather data). To align these spatial scales, ZIP code–level data were assigned to all feeders within the same ZIP code, and station-level data were mapped to ZIP codes using the nearest weather station for each ZIP code." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Cleaning and Validating CIMIS Data:\n", "\n", "To match the temporal structure of the modeling dataset, which operates at the month–hour level, the original hourly CIMIS observations were aggregated by computing average values for each month–hour combination (for example, the average of all observations at 2:00 PM in January for a specific CIMIS station). \n", "\n", "Spatially, CIMIS data are reported at individual weather stations rather than at the feeder level. Because latitude and longitude coordinates were available for each CIMIS station, and a PG&E territory ZIP code shapefile was available, spatial aggregation was performed using geographic proximity. Each CIMIS station was spatially joined to a ZIP code using its coordinates, and for each ZIP code the nearest station was identified. The resulting month–hour weather variables were then assigned to all feeders located within the corresponding ZIP code. This ensured that each feeder was associated with representative local weather conditions while maintaining geographic consistency across the PG&E territory.\n", "\n", "Following these cleaning, aggregation, and spatial alignment steps, the final CIMIS dataset provided complete and spatially consistent month–hour profiles of solar radiation, air temperature, and wind speed for use as continuous predictors in the headroom prediction models.\n", "\n", "For some CIMIS stations, missing values were present for specific hours and for one or more weather sensors. These rows were not removed during the cleaning process, as doing so would have created temporal gaps in the month–hour weather profiles and reduced data coverage across the study period. Instead, missing values were handled during the modeling stage. Specifically, indicator variables (via dummy encoding) were created for missing entries, and the corresponding values were imputed using average conditions. This approach preserved the completeness of the dataset while minimizing potential bias introduced by missing sensor readings.\n", "\n", "After cleaning and merging, the weather_ready_to_merge looks like:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ZIP_CODEMonthHourSol Rad (Ly/day)Air Temp (F)Wind Speed (mph)
09000111000.00053.8262.623
19000112000.00053.6212.629
29000113000.00053.5372.394
390001140099.61953.2472.484
490001150013.90553.2422.745
\n", "
" ], "text/plain": [ " ZIP_CODE Month Hour Sol Rad (Ly/day) Air Temp (F) Wind Speed (mph)\n", "0 90001 1 100 0.000 53.826 2.623\n", "1 90001 1 200 0.000 53.621 2.629\n", "2 90001 1 300 0.000 53.537 2.394\n", "3 90001 1 400 99.619 53.247 2.484\n", "4 90001 1 500 13.905 53.242 2.745" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "df = pd.read_csv(\"weather/2024_weather_cleaned.csv\")\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Cleaning and Validating EV Data:\n", "\n", "We chose to clean the California Energy Commission EV population dataset in Excel, as the dataset is static and the transformation steps were straightforward to implement and replicate using spreadsheet tools. First, we filtered the raw ZIP-level table to retain only the two most recent years (2023 and 2024) and only the vehicle types relevant to electric-grid impacts: Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs). We then used an Excel pivot table to aggregate the total number of BEVs and PHEVs in each ZIP code for each year. From this pivot output, we constructed a new table containing one row per ZIP code with fields for the 2023 EV count, the 2024 EV count, and the absolute change between the two years (EV_2024 − EV_2023). Although we initially calculated the percent change, we ultimately excluded this variable due to high collinearity with both absolute change and the raw EV counts, which provided redundant information and reduced interpretability.\n", "\n", "This pivot-based approach provided a transparent way to manually inspect intermediate tables and verify that values aligned with expectations—particularly for ZIP codes with unusually high or low EV counts or ZIPs present in only one of the two years. We checked a sample of ZIP codes in the original CEC file against the pivot output to ensure that totals matched the underlying registration records and that no EV-bearing ZIP codes were inadvertently excluded. We also confirmed that missing values reflected genuine absence of BEV/PHEV activity rather than data loss. Because the dataset is static and well-structured, this manual validation process helped ensure that the cleaned dataset did not contain structural issues (e.g., malformed ZIP codes, duplicate entries, or incomplete year coverage) that would compromise downstream analysis.\n", "\n", "After validating the pivot output, we exported it as a clean CSV file (EV_Pop_Growth_23_24.csv). In Python, we loaded this CSV and merged it into the primary modeling DataFrame using a standardized five-digit ZIP code key. Although we replicated the cleaning logic in Python for reproducibility, the Excel workflow remained the canonical preprocessing step because it was simple, transparent, and sufficient for a once-per-year dataset. Python was then used only for merging and model-ready transformations.\n", "\n", "After cleaning and merging, the EV_ready_to_merge looks like:\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Zip Code20232024YoY Change%change
0890611.01.000.000000
190001295.0449.01540.522034
290002253.0396.01430.565217
390003318.0507.01890.594340
4900041775.02241.04660.262535
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" ], "text/plain": [ " Zip Code 2023 2024 YoY Change %change\n", "0 89061 1.0 1.0 0 0.000000\n", "1 90001 295.0 449.0 154 0.522034\n", "2 90002 253.0 396.0 143 0.565217\n", "3 90003 318.0 507.0 189 0.594340\n", "4 90004 1775.0 2241.0 466 0.262535" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "df = pd.read_csv(\"EV_Pop_Growth_23_24.csv\")\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ICA Data Cleaning and Merge Pipeline\n", "\n", "1.\tClimate Zones and ZIP Code Cleaning\n", "The merge pipeline begins by loading California Building Climate Zone shapefiles and ZIP code boundary shapefiles. Both datasets were converted into a shared coordinate reference system to ensure spatial comparability. A spatial intersection was performed to determine which climate zone overlapped most with each ZIP code. ZIPs were assigned to the climate zone with the largest intersecting area instead of centroid, since ZIP shapes are often irregular. The resulting ZIP–climate mapping was validated by verifying that each ZIP received exactly one climate zone assignment and that the set of climate zone labels matched CALMAC documentation. \n", "\n", "2.\tCALMAC Load Shape Cleaning\n", "CALMAC residential load shapes were provided as hourly time series for 160 GP profiles. Timestamps were parsed into datetime objects, and hour and month fields were extracted to match the temporal structure of the ICA data. The dataset was then aggregated to gp × month × hour by computing average hourly consumption within each group. Climate zone assignments were merged into this dataset so that only GP profiles corresponding to the assigned climate zone for each ZIP were retained. Consistency checks confirmed that the aggregated table contained the expected number of gp × month × hour combinations and that the climate zone mapping aligned with CALMAC descriptions.\n", "\n", "3.\tFeeder Shapefile Cleaning and ZIP Assignment\n", "Feeder polygons from PG&E’s FeederDetail_Voltage shapefile were loaded and feederIDs were normalized to remove any leading zeros or extra spacing. A spatial overlay was used to assign each feeder to the ZIP code representing the largest area of overlap. Approximately two-thirds of feeder polygons did not intersect any ZIP boundary in the shapefile. The definitive reason for this is unknown, but we assume that the missing feeders were in rural areas with low population, and therefore may not have a zip code. However, all feeders that appeared in later merges were successfully assigned ZIP codes, confirming that ZIP assignment was not a source of missing feeders.\n", "\n", "4.\tConstructing the Feeder Load Matrix\n", "The ZIP–GP lookup was merged with the aggregated CALMAC load shapes to produce feeder–GP–month–hour observations. This dataset was pivoted wide to create one row per feeder × month × hour with one column per GP. GP columns were then summed to compute a single aggregated hourly load value for each feeder–month–hour. This produced a consistent structure for merging with ICA and other features and ensured that non-overlapping GP profiles did not create irregular row counts.\n", "\n", "5.\tEV Data Cleaning and Merge\n", "ZIP-level EV adoption data for 2023 and 2024 were prepared externally by selecting Battery Electric Vehicle and Plug-in Hybrid Electric Vehicle categories, calculating year-over-year absolute and percentage change, and standardizing ZIP codes to five-digit strings. Duplicates were removed, and column names were matched to the main dataset. During the merge, ZIP codes were normalized in both datasets to prevent mismatch due to inconsistent string formatting.\n", "\n", "6.\tICA Data Cleaning and Validation\n", "ICA data arrived in the form of PG&E “division ZIPs,” each containing hundreds of feeder-specific ZIP files. Each inner ZIP held a CSV representing hourly hosting capacity for each line section. Column names were normalized to lowercase and stripped of whitespace. Files missing required fields (month, hour, loadorgen, the selected ICA column) were skipped. Exploratory validation across tens of millions of rows showed that all ICA observations in the raw data were labeled as generation (“G”). No load rows contain headroom data, even when scanning both GICA and LICA file structures. This confirmed that load-side headroom could not be derived from the available data and that generation hosting capacity (hourly_ica_sg) was the only viable ICA measure.\n", "\n", "ICA values were aggregated across line sections by computing the mean hosting capacity for each feederid × month × hour. Approximately 7.4% of month-hour combinations lacked any corresponding ICA rows in PG&E source files. Missingness checks showed no pattern across hours or months, indicating the gaps originate from PG&E’s dataset rather than the processing pipeline.\n", "\n", "Feeder IDs extracted from ICA ZIP filenames were normalized by removing leading zeros and trimming whitespace. This normalization ensured that ICA feeder IDs aligned with those in the feeder shapefile and feeder_characteristics metadata file.\n", "\n", "7.\tMerging ICA, Feeder Metadata, and Weather\n", "The aggregated ICA data were merged onto the feeder load matrix using feederid, month, and hour as keys. Rows with missing ICA were subsequently filtered out to create the final cleaned dataset. Feeder-level metadata from feeder_characteristics.csv was merged by normalizing both feederid columns and removing existing metadata columns to avoid duplicate suffixes. A validation step confirmed that all feeder-level metadata columns merged correctly after correcting for leading zeros in feeder IDs. Finally, weather data were cleaned by converting 100-, 200-, …-formatted hour stamps into 0–23 integers and merged on ZIP × month × hour.\n", "\n", "8.\tFinal Validation\n", "Several validation checks ensured the merged dataset was internally consistent. These included verifying that each feeder-month-hour key appeared only once, confirming that feeder IDs in the final dataset matched those in both ICA files and feeder metadata, and confirming that feeders had an expected distribution across PG&E service territory.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\"\"\" Goal: produce a feeder × month-hour feature matrix with:\n", "- Load shape features (kWh per GP)\n", "- EV adoption (via ZIP)\n", "- Temperature (via ZIP or grid)\n", "- ICA / feeder metadata (line rating, %res, %ind, %com, congestion Y/N)\n", "\"\"\"\n", "\n", "import pandas as pd\n", "import geopandas as gpd\n", "import glob\n", "import os\n", "import zipfile\n", "from io import BytesIO\n", "\n", "#remove leading '0's from feederid\n", "def normalize_feederid(series):\n", " \"\"\"\n", " Normalize feeder ids to a common key:\n", " - cast to string\n", " - strip whitespace\n", " - strip leading zeros (\"001234\" -> \"1234\")\n", " - map empty string to \"0\"\n", " \"\"\"\n", " s = series.astype(str).str.strip()\n", " s_nozero = s.str.lstrip(\"0\")\n", " return s_nozero.replace({\"\": \"0\"})\n", "\n", "def load_feeder_characteristics(path=\"ica_data/feeder_characteristics.csv\"):\n", " \"\"\"\n", " Load feeder-level data, including DER capacity, customer mix, etc. and drop unneeded columns.\n", "\n", " \"\"\"\n", " print(\"Loading feeder characteristics...\")\n", "\n", " df = pd.read_csv(path)\n", "\n", " df.columns = df.columns.str.strip().str.lower()\n", "\n", " if \"feederid\" not in df.columns:\n", " raise ValueError(\"feeder_characteristics must contain a 'Feeder ID' column.\")\n", " drop_cols = [\n", " \"feeder_name\",\n", " \"substation name\",\n", " \"division\",\n", " \"last_update_on_map\",\n", " \"publish\",\n", " \"objectid\",\n", " \"nominal_voltage_kV\", #this would probably be a strong predictor, but removing it for the transferrability of our model\n", " \"redacted_data\",\n", " \"shape__length\",\n", "\n", " ]\n", " drop_cols = [c for c in drop_cols if c in df.columns]\n", " df = df.drop(columns=drop_cols)\n", "\n", " \n", " df[\"feederid\"] = normalize_feederid(df[\"feederid\"])\n", "\n", " return df\n", "\n", "def process_line_zips(\n", " input_dir, loading_scenario, ica_col, debug: bool = False,\n", "):\n", " \"\"\"\n", " Stream through PG&E division ZIPs and compute feeder-level\n", " ICA data.\n", "\n", " Returns a DataFrame with columns:\n", " ['feederid', 'month', 'hour', 'loadorgen', 'mean_ica_sg']\n", " \"\"\"\n", " records = []\n", "\n", " # Find division-level ZIP files\n", " division_zips = glob.glob(os.path.join(input_dir, \"*.zip\"))\n", " if not division_zips:\n", " print(f\"[ICA] No division ZIPs found in {input_dir}\")\n", " return pd.DataFrame(columns=[\"feederid\", \"month\", \"hour\", \"loadorgen\", \"mean_ica_sg\"])\n", "\n", " if debug: # only process first division .zip in debug mode\n", " division_zips = division_zips[:1]\n", "\n", " for div_zip_path in division_zips:\n", " print(f\"[ICA] Processing division zip: {os.path.basename(div_zip_path)}\")\n", " with zipfile.ZipFile(div_zip_path, \"r\") as div_zip:\n", " for inner_name in div_zip.namelist():\n", " # Only process inner ZIPs\n", " if not inner_name.lower().endswith(\".zip\"):\n", " continue\n", "\n", " # Get feederid from inner ZIP name (zip files hold line sections, title = feederID)\n", " feeder_id_raw = os.path.splitext(os.path.basename(inner_name))[0]\n", " # Handle names like 'GICA_102530401' - GICA = Generation data, LICA = Load data\n", " parts = feeder_id_raw.split(\"_\")\n", " if len(parts) > 1:\n", " feederid = parts[-1]\n", " else:\n", " feederid = feeder_id_raw\n", "\n", " try:\n", " inner_bytes = div_zip.read(inner_name)\n", " except KeyError:\n", " print(f\"[ICA] Warning: could not read {inner_name} in {div_zip_path}\")\n", " continue\n", "\n", " with zipfile.ZipFile(BytesIO(inner_bytes), \"r\") as feeder_zip:\n", " csv_members = [\n", " m for m in feeder_zip.namelist()\n", " if m.lower().endswith(\".csv\")\n", " ]\n", " if not csv_members:\n", " print(f\"[ICA] Warning: no CSV in {inner_name}\")\n", " continue\n", "\n", " csv_name = csv_members[0]\n", " with feeder_zip.open(csv_name) as src:\n", " df = pd.read_csv(src)\n", "\n", " # Standardize columns\n", " df.columns = df.columns.str.strip().str.lower()\n", "\n", " # Ensure ICA column exists\n", " if ica_col not in df.columns:\n", " print(f\"[ICA] Warning: '{ica_col}' not found in {csv_name}; skipping this file\")\n", " continue\n", "\n", " # Ensure required grouping columns exist\n", " required_group_cols = [\"month\", \"hour\", \"loadorgen\"]\n", " missing = [c for c in required_group_cols if c not in df.columns]\n", " if missing:\n", " print(f\"[ICA] Warning: missing {missing} in {csv_name}; skipping this file\")\n", " continue\n", "\n", " # Filter by loading scenario (e.g., 90th percentile)\n", " if \"loading_scenario\" in df.columns and loading_scenario is not None:\n", " df = df[df[\"loading_scenario\"] == loading_scenario].copy()\n", "\n", " # Drop monthly_ica_sg if present\n", " if \"monthly_ica_sg\" in df.columns:\n", " df = df.drop(columns=[\"monthly_ica_sg\"])\n", "\n", " # Attach feederid\n", " df[\"feederid\"] = feederid\n", "\n", " # Coerce ICA column to numeric and drop NAs\n", " df[ica_col] = pd.to_numeric(df[ica_col], errors=\"coerce\")\n", " df = df.dropna(subset=[ica_col])\n", " if df.empty:\n", " continue\n", "\n", " group_cols = [\"feederid\", \"month\", \"hour\", \"loadorgen\"]\n", "\n", " # Aggregate across line sections to feeder-level mean\n", " grouped = (\n", " df.groupby(group_cols, as_index=False)[ica_col]\n", " .mean()\n", " .rename(columns={ica_col: \"mean_ica_sg\"})\n", " )\n", "\n", " records.append(grouped)\n", "\n", " if not records:\n", " raise ValueError(\"[ICA] No ICA values computed from division ZIPs.\")\n", "\n", " # First, concatenate all partial results\n", " feeder_ica = pd.concat(records, ignore_index=True)\n", "\n", " # Normalize feeder IDs\n", " feeder_ica[\"feederid\"] = normalize_feederid(feeder_ica[\"feederid\"])\n", "\n", " # Some feeders may appear in multiple files; aggregate again\n", " feeder_ica = (\n", " feeder_ica\n", " .groupby([\"feederid\", \"month\", \"hour\", \"loadorgen\"], as_index=False)\n", " .agg({\"mean_ica_sg\": \"mean\"})\n", " )\n", "\n", " print(\n", " f\"[ICA] Computed feeder-level ICA for \"\n", " f\"{feeder_ica['feederid'].nunique()} feeders \"\n", " f\"across {feeder_ica['loadorgen'].unique().tolist()}.\"\n", " )\n", "\n", " return feeder_ica\n", "\n", "def load_calmac_load_shapes():\n", " \"\"\"\n", " Load CALMAC hourly load shapes. 160 total residential load shapes, 40 per climate zone.\n", "\n", " CSV shoudl contain [gp (load shape identifier), date, hour, kwh]\n", " \"\"\"\n", " print(\"Loading residential electric load shapes from CALMAC/Res_GP_Elec_2024.csv...\")\n", " load_data = pd.read_csv(\"CALMAC/Res_GP_Elec_2024.csv\")\n", " return load_data\n", "\n", "\n", "# Load spatial data\n", "def load_climate_zones():\n", " \"\"\"Load CEC building climate zones shapefile. \n", " This is used to spacially map load shapes (GPs) and assign to correct ZIP code\"\"\"\n", " print(\"Loading climate zones...\")\n", " climate_zones = gpd.read_file(\"CALMAC/Building_Climate_Zones.shp\")\n", " return climate_zones\n", "\n", "def load_zip_polygons():\n", " \"\"\"Load ZIP code polygons shapefile. Used to determine which ZIPs/Climate zones belong together\"\"\"\n", " print(\"Loading ZIP polygons...\")\n", " zips = gpd.read_file(\"zip_codes/zip_poly.shp\")\n", " return zips\n", "\n", " \n", "def load_feeder_shapes():\n", " \"\"\"\n", " Load feeder shapefile. Used to Join with ZIP codes.\n", " \"\"\"\n", " print(\"Loading feeders...\")\n", " feeders = gpd.read_file(\"ica_data/FeederDetail_Voltage.shp\") \n", " feeders[\"feederid\"] = normalize_feederid(feeders[\"feederid\"])\n", " return feeders\n", "\n", "# Map ZIP -> climate zone --> GP list\n", "def map_climate_zones(climate_zones: gpd.GeoDataFrame) -> gpd.GeoDataFrame:\n", " \"\"\"\n", " CALMAC documentation says which Climate Zones correspond to their 4 PG&E service territories.\n", " Add CALMAC climate zone (extracted from (GP)) labels to CEC climate zones.\n", " \"\"\"\n", " print(\"Mapping climate zones to CALMAC groups...\")\n", " cz_groups = {\n", " 1: \"Coastal\", 3: \"Coastal\", 5: \"Coastal\",\n", " 2: \"Inland\", 4: \"Inland\",\n", " 11: \"North Central Valley\", 12: \"North Central Valley\",\n", " 13: \"South Central Valley\",\n", " }\n", "\n", " climate_zones[\"BZone\"] = climate_zones[\"BZone\"].astype(int)\n", " climate_zones[\"cz_groups\"] = climate_zones[\"BZone\"].map(cz_groups)\n", " return climate_zones\n", "\n", "\n", "def process_zip_climate_mapping(zips: gpd.GeoDataFrame, climate_zones: gpd.GeoDataFrame) -> gpd.GeoDataFrame:\n", " \"\"\"\n", " Assign each ZIP a climate zone group based on the CZ that has the largest overlap area.\n", " Returns a GeoDataFrame with ZIP geometry and cz_groups.\n", " \"\"\"\n", " print(\"Processing ZIP → climate group mapping (majority-area overlay)...\")\n", " # Ensure both layers are in the same CRS for area calculations\n", " zips = zips.to_crs(climate_zones.crs)\n", "\n", " # Intersect ZIP polygons with climate zone polygons to get overlap pieces\n", " zip_cz_overlap = gpd.overlay(\n", " zips,\n", " climate_zones[[\"cz_groups\", \"geometry\"]],\n", " how=\"intersection\",\n", " )\n", "\n", " # Compute area of each overlap piece\n", " zip_cz_overlap[\"overlap_area\"] = zip_cz_overlap.geometry.area\n", "\n", " # For each ZIP, find the climate group with the largest overlapping area\n", " # Assumes ZIP_CODE uniquely identifies each ZIP polygon\n", " idx = zip_cz_overlap.groupby(\"ZIP_CODE\")[\"overlap_area\"].idxmax()\n", " majority = zip_cz_overlap.loc[idx, [\"ZIP_CODE\", \"cz_groups\"]]\n", "\n", " # Join the primary climate group back onto the original ZIP geometries\n", " zips_climate = zips.merge(majority, on=\"ZIP_CODE\", how=\"left\")\n", " zips_climate = zips_climate[zips_climate[\"cz_groups\"].notna()].copy()\n", " return zips_climate # has ZIP_CODE, geometry, cz_groups, etc.\n", "\n", "\n", "def load_calmac_characteristics():\n", " \"\"\"\n", " Load CALMAC residential and non-residential GP characteristics and\n", " return a combined DataFrame with 'gp' and climate zone columns.\n", " \"\"\"\n", " print(\"Loading CALMAC characteristics...\")\n", " res_chars = pd.read_csv(\"CALMAC/res_characteristics.csv\")\n", " nonres_chars = pd.read_csv(\"CALMAC/nonres_characteristics.csv\")\n", "\n", " res_chars[\"type\"] = \"residential\"\n", " nonres_chars[\"type\"] = \"nonresidential\"\n", "\n", " gps_all = pd.concat([res_chars, nonres_chars], ignore_index=True)\n", " return gps_all\n", "\n", "\n", "def zip_gp_lookup(zips_climate: gpd.GeoDataFrame, gps_all: pd.DataFrame) -> pd.DataFrame:\n", " \"\"\"\n", " Build a ZIP → GP lookup table no geometry.\n", "\n", " Steps:\n", " - Group GPs by CALMAC climate zone label (seg_cz)\n", " - Merge onto ZIPs by cz_groups (must match seg_cz labels: e.g. 'Coastal')\n", " - Explode GP lists so we get one row per ZIP–GP pair\n", " \"\"\"\n", " print(\"Building ZIP → GP lookup (no geometry)...\")\n", "\n", " # Group GPs by CALMAC segment climate zone, create list of relevant GPs per CZ\n", " gps_by_zone = gps_all.groupby(\"seg_cz\")[\"gp\"].apply(list).reset_index()\n", " gps_by_zone.rename(columns={\"gp\": \"gp_list\"}, inplace=True)\n", "\n", " # keep only necessary columns\n", " zips_small = zips_climate[[\"ZIP_CODE\", \"cz_groups\"]].drop_duplicates()\n", " # zips_climate has a 'cz_groups' column, match to 'seg_cz'\n", " zip_gp = zips_small.merge(\n", " gps_by_zone,\n", " left_on=\"cz_groups\",\n", " right_on=\"seg_cz\",\n", " how=\"left\"\n", " )\n", "\n", " zip_gp = zip_gp.drop(columns=[\"seg_cz\"])\n", "\n", " # Explode gp_list to one row per ZIP–GP\n", " zip_gp = zip_gp.explode(\"gp_list\").rename(columns={\"gp_list\": \"gp\"})\n", "\n", " print(f\"ZIP–GP pairs: {len(zip_gp)}\")\n", " return zip_gp \n", "\n", "# Aggregate KWH consumed across gp x month x hour\n", "def aggregate_load_shapes():\n", " print(\"Loading CALMAC load shapes...\")\n", " load_data = load_calmac_load_shapes()\n", "\n", " # Ensure date is datetime\n", " load_data[\"date\"] = pd.to_datetime(load_data[\"date\"])\n", "\n", " load_data[\"month\"] = load_data[\"date\"].dt.month\n", "\n", " # Aggregate to month-hour-gp\n", " load_month_hour = (\n", " load_data\n", " .groupby([\"gp\", \"month\", \"hour\"], as_index=False)\n", " .agg({\"kwh\": \"mean\"}) \n", " )\n", "\n", " print(\n", " f\"Aggregated load rows: {len(load_month_hour)} \"\n", " f\"({load_month_hour['gp'].nunique()} GPs, \"\n", " f\"{load_month_hour['month'].nunique()} months, \"\n", " f\"{load_month_hour['hour'].nunique()} hours)\"\n", " )\n", " return load_month_hour # columns: gp, month, hour, kwh\n", "\n", "# Map feeders to zips\n", "\n", "def feeder_zips_map(feeders: gpd.GeoDataFrame, zips_climate: gpd.GeoDataFrame) -> gpd.GeoDataFrame:\n", " \"\"\"Map each feeder to a ZIP using majority-area overlay.\"\"\"\n", " print(\"Mapping feeders to ZIPs (majority-area overlay)...\")\n", "\n", " # Ensure same CRS\n", " feeders = feeders.to_crs(zips_climate.crs)\n", "\n", " # Intersect feeder polygons with ZIP polygons to get overlap pieces\n", " feeder_zip_overlap = gpd.overlay(\n", " feeders[[\"feederid\", \"geometry\"]],\n", " zips_climate[[\"ZIP_CODE\", \"geometry\"]],\n", " how=\"intersection\",\n", " )\n", "\n", " # Compute area of each overlap piece\n", " feeder_zip_overlap[\"overlap_area\"] = feeder_zip_overlap.geometry.area\n", "\n", " # For each feeder, find the ZIP with the largest overlapping area\n", " idx = feeder_zip_overlap.groupby(\"feederid\")[\"overlap_area\"].idxmax()\n", " feeder_zip = feeder_zip_overlap.loc[idx, [\"feederid\", \"ZIP_CODE\"]]\n", "\n", " # Only one ZIP per feeder \n", " feeder_zip_map = feeder_zip.reset_index(drop=True)\n", "\n", " print(f\"Unique feeders mapped: {feeder_zip_map['feederid'].nunique()}\")\n", " return feeder_zip_map\n", "\n", "# Pivot wide - necessary to keep DF from exploding\n", "def build_feeder_gp(zip_gp: pd.DataFrame, load_month_hour: pd.DataFrame, feeder_zip_map: pd.DataFrame) -> pd.DataFrame:\n", " \"\"\"\n", " Build feeder-level load features. Final shape: one row per feederid, ZIP, month, hour)\n", " \"\"\"\n", " print(\"Building feeder × month-hour load features...\")\n", " # keep only zips with feeders\n", " zips_for_feeders = feeder_zip_map[\"ZIP_CODE\"].unique()\n", " zip_gp_sub = zip_gp[zip_gp[\"ZIP_CODE\"].isin(zips_for_feeders)].copy()\n", "\n", " # Join ZIP → feeder to get feeder–ZIP–GP\n", " feeder_zip_gp = feeder_zip_map.merge(\n", " zip_gp_sub,\n", " on=\"ZIP_CODE\",\n", " how=\"left\"\n", " )\n", " \n", " # feeder-gp pairs\n", " feeder_gp = (\n", " feeder_zip_gp[[\"feederid\", \"gp\"]].dropna(subset=[\"gp\"]).drop_duplicates()\n", " )\n", " print(f\"Feeder-GP pairs: {len(feeder_gp)}\")\n", "\n", " #join feeder-GP with loads\n", " feeder_gp_month_hour = feeder_gp.merge(\n", " load_month_hour,\n", " on=\"gp\",\n", " how=\"left\"\n", " )\n", " #pivot GP to columns: one row per feeder-month-hour\n", " feeder_wide = feeder_gp_month_hour.pivot_table(\n", " index=[\"feederid\", \"month\", \"hour\"],\n", " columns=\"gp\",\n", " values=\"kwh\",\n", " aggfunc=\"mean\", #shouldn't be duplicates, but pivot_wide requires an aggfunc\n", " #GPs that don't belong to a feeder will be \"NaN\"\n", " ).reset_index()\n", "\n", " # flatten GP columns, rename kwh_gp\n", " feeder_wide.columns = [\n", " f\"kwh_{c}\" if isinstance(c, str) and not c in {\"feederid\", \"month\", \"hour\"} else c\n", " for c in feeder_wide.columns\n", " ]\n", "\n", " # Sum across all kwh_* columns to get a single load shape per feeder-month-hour\n", " gp_cols = [c for c in feeder_wide.columns if isinstance(c, str) and c.startswith(\"kwh_\")]\n", " feeder_wide[\"load\"] = feeder_wide[gp_cols].sum(axis=1)\n", "\n", " # Keep only aggregated load plus keys\n", " feeder_wide = feeder_wide[[\"feederid\", \"month\", \"hour\", \"load\"]]\n", "\n", " # Merge ZIP_CODE back in\n", " feeder_wide = feeder_wide.merge(\n", " feeder_zip_map,\n", " on=\"feederid\",\n", " how=\"left\"\n", " )\n", "\n", " print(f\"Feeder-wide feature rows: {len(feeder_wide)} with aggregated load shape\")\n", " return feeder_wide\n", "\n", "def load_ev_data(ev_path: str = \"EV_Pop_Growth_23_24.csv\") -> pd.DataFrame:\n", " \"\"\"\n", " Load EV adoption data and normalize ZIP code.\n", "\n", " Expects a column 'Zip Code' in the CSV.\n", " Returns a DataFrame with a standardized 'ZIP_CODE' column.\n", " \"\"\"\n", " print(\"Loading EV data...\")\n", " ev = pd.read_csv(ev_path)\n", "\n", " # Normalize ZIP codes to 5-char str\n", " ev[\"Zip Code\"] = (\n", " ev[\"Zip Code\"]\n", " .astype(str)\n", " .str.strip()\n", " .str.zfill(5)\n", " )\n", "\n", " # Drop duplicate ZIPs \n", " ev_unique = ev.drop_duplicates(subset=[\"Zip Code\"]).copy()\n", "\n", " # Rename to match feeder_features\n", " ev_unique = ev_unique.rename(columns={\"Zip Code\": \"ZIP_CODE\"})\n", "\n", " print(f\"Unique ZIPs in EV data: {ev_unique['ZIP_CODE'].nunique()}\")\n", " return ev_unique\n", "def attach_ev_to_feeders(feeder_features: pd.DataFrame, ev_df: pd.DataFrame) -> pd.DataFrame:\n", " \"\"\"\n", " Merge ZIP-level EV data onto feeder_features (feeder × month × hour).\n", "\n", " - feeder_features: must have 'ZIP_CODE'\n", " - ev_df: must have 'ZIP_CODE' + EV columns\n", "\n", " Result: same number of rows as feeder_features, with EV columns added.\n", " \"\"\"\n", " print(\"Merging EV data onto feeder_features...\")\n", "\n", " # Normalize ZIP format in feeder_features \n", " ff = feeder_features.copy()\n", " ff[\"ZIP_CODE\"] = (\n", " ff[\"ZIP_CODE\"]\n", " .astype(str)\n", " .str.strip()\n", " .str.zfill(5)\n", " )\n", "\n", " merged = ff.merge(\n", " ev_df,\n", " how=\"left\",\n", " on=\"ZIP_CODE\",\n", " suffixes=(\"\", \"_EV\") # EV side gets suffix if there are name conflicts\n", " )\n", "\n", " print(f\"Rows before EV merge: {len(ff):,}\")\n", " print(f\"Rows after EV merge: {len(merged):,}\")\n", " return merged\n", "\n", "def main(debug: bool = False):\n", " # Load climate + ZIP\n", " climate_zones = load_climate_zones()\n", " climate_zones = map_climate_zones(climate_zones)\n", "\n", " zips = load_zip_polygons()\n", " zips_climate = process_zip_climate_mapping(zips, climate_zones)\n", "\n", " # 2. CALMAC GPs + ZIP to GP mapping\n", " gps_all = load_calmac_characteristics()\n", " zip_gp = zip_gp_lookup(zips_climate, gps_all)\n", "\n", " # 3. Aggregate CALMAC load shapes to gp × month × hour (May–Oct)\n", " load_month_hour = aggregate_load_shapes()\n", "\n", " # 4. Load feeders + map to ZIPs\n", " feeders = load_feeder_shapes()\n", " feeder_zip_map = feeder_zips_map(feeders, zips_climate)\n", "\n", " # 5. Build feeder × month-hour × GP load matrix\n", " feeder_features = build_feeder_gp(zip_gp, load_month_hour, feeder_zip_map)\n", " #6. Load EV data and merge onto feeder_features\n", " ev_df = load_ev_data()\n", " feeder_features = attach_ev_to_feeders(feeder_features, ev_df)\n", "\n", " feeder_ica = process_line_zips(\n", " input_dir=\"ica_data/raw_zips\",\n", " loading_scenario=90,\n", " ica_col=\"hourly_ica_sg\",\n", " debug=debug,\n", " )\n", "\n", " feeder_features = feeder_features.merge(\n", " feeder_ica,\n", " on=[\"feederid\", \"month\", \"hour\"],\n", " how=\"left\",\n", " )\n", " \n", " # 7. Load feeder-level metadata and merge onto every feeder × month × hour row\n", " feeder_chars = load_feeder_characteristics()\n", "\n", " # Ensure feederid is a normalized string key on both sides before merging\n", " feeder_features[\"feederid\"] = (\n", " feeder_features[\"feederid\"].astype(str).str.strip()\n", " )\n", " feeder_chars[\"feederid\"] = (\n", " feeder_chars[\"feederid\"].astype(str).str.strip()\n", " )\n", " feeder_features[\"feederid\"] = normalize_feederid(feeder_features[\"feederid\"])\n", " feeder_chars[\"feederid\"] = normalize_feederid(feeder_chars[\"feederid\"])\n", " \n", " feeder_features = feeder_features.merge(\n", " feeder_chars,\n", " on=\"feederid\",\n", " how=\"left\"\n", " )\n", "\n", " print(\"Loading weather data...\")\n", " weather = pd.read_csv(\"weather/2024_weather_cleaned.csv\")\n", "\n", " # Normalize column names\n", " weather.rename(columns={\n", " \"Month\": \"month\",\n", " \"Hour\": \"hour\"\n", " }, inplace=True)\n", " # Convert weather hour column from 100,200,... to 1,2,... or 0–23 range\n", " weather[\"hour\"] = (weather[\"hour\"] // 100).astype(int)\n", " # Fix 24 → hour 0\n", " weather.loc[weather[\"hour\"] == 24, \"hour\"] = 0\n", "\n", " # Normalize ZIP + month again after adjusting hour\n", " weather[\"ZIP_CODE\"] = weather[\"ZIP_CODE\"].astype(str).str.zfill(5)\n", " weather[\"month\"] = weather[\"month\"].astype(int)\n", " # Ensure types match for merge keys\n", " weather[\"ZIP_CODE\"] = weather[\"ZIP_CODE\"].astype(str).str.zfill(5)\n", " weather[\"month\"] = weather[\"month\"].astype(int)\n", " weather[\"hour\"] = weather[\"hour\"].astype(int)\n", "\n", " print(\"Merging weather data...\")\n", " feeder_features = feeder_features.merge(\n", " weather,\n", " on=[\"ZIP_CODE\", \"month\", \"hour\"],\n", " how=\"left\"\n", " )\n", "\n", " print(\"Weather coverage:\", weather[\"ZIP_CODE\"].nunique(), \"ZIPs in weather file\")\n", " print(\"After merge, rows with weather:\", feeder_features[\"Sol Rad (Ly/day)\"].notna().sum())\n", "\n", " # 8. Save in repo\n", " os.makedirs(\"outputs\", exist_ok=True)\n", " feeder_features.to_csv(\"outputs/feeder_load_features.csv\", index=False)\n", " print(\"Saved outputs/feeder_load_features.csv\")\n", "\n", " return feeder_features \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "feeder_features = main(debug=False) #debug = TRUE for faster processing (only one PGE zip file)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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feederidmonthhourloadZIP_CODE20232024YoY Change%changeloadorgen...industrial customer countagricultural customer countother customersexisting distributed generation (kw)queued distributed generation (kw)total distributed generation (kw)voltageSol Rad (Ly/day)Air Temp (F)Wind Speed (mph)
01020411031.00.034.14793295928610.0727.0117.00.191803G...16229.06030420010230120.047.4393.852
11020411031.01.032.08356595928610.0727.0117.00.191803G...16229.06030420010230120.047.1743.587
21020411031.02.030.88502995928610.0727.0117.00.191803G...16229.06030420010230120.046.9193.945
31020411031.03.030.65882495928610.0727.0117.00.191803G...16229.06030420010230120.046.7554.213
41020411031.04.031.62599495928610.0727.0117.00.191803G...16229.06030420010230120.046.7264.129
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5 rows × 25 columns

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" ], "text/plain": [ " feederid month hour load ZIP_CODE 2023 2024 YoY Change \\\n", "0 102041103 1.0 0.0 34.147932 95928 610.0 727.0 117.0 \n", "1 102041103 1.0 1.0 32.083565 95928 610.0 727.0 117.0 \n", "2 102041103 1.0 2.0 30.885029 95928 610.0 727.0 117.0 \n", "3 102041103 1.0 3.0 30.658824 95928 610.0 727.0 117.0 \n", "4 102041103 1.0 4.0 31.625994 95928 610.0 727.0 117.0 \n", "\n", " %change loadorgen ... industrial customer count \\\n", "0 0.191803 G ... 162 \n", "1 0.191803 G ... 162 \n", "2 0.191803 G ... 162 \n", "3 0.191803 G ... 162 \n", "4 0.191803 G ... 162 \n", "\n", " agricultural customer count other customers \\\n", "0 2 9.0 \n", "1 2 9.0 \n", "2 2 9.0 \n", "3 2 9.0 \n", "4 2 9.0 \n", "\n", " existing distributed generation (kw) queued distributed generation (kw) \\\n", "0 6030 4200 \n", "1 6030 4200 \n", "2 6030 4200 \n", "3 6030 4200 \n", "4 6030 4200 \n", "\n", " total distributed generation (kw) voltage Sol Rad (Ly/day) Air Temp (F) \\\n", "0 10230 12 0.0 47.439 \n", "1 10230 12 0.0 47.174 \n", "2 10230 12 0.0 46.919 \n", "3 10230 12 0.0 46.755 \n", "4 10230 12 0.0 46.726 \n", "\n", " Wind Speed (mph) \n", "0 3.852 \n", "1 3.587 \n", "2 3.945 \n", "3 4.213 \n", "4 4.129 \n", "\n", "[5 rows x 25 columns]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "feeder_features.head(5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "np.float64(0.0)" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking for NAs\n", "feeder_features[\"loadorgen\"].isna().mean()\n", "feeder_features[\"feederid\"].str.startswith(\"0\").mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Summary and Exploratory Data Analysis (10 points)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The exploratory distributions reveal substantial heterogeneity in both system demand and key explanatory variables. Feeder load exhibits a wide, right-skewed distribution with a strong concentration around moderate values and a long upper tail, indicating that while most feeder–hour observations operate under typical demand levels, a smaller subset experiences very high load that likely drives congestion risk. Solar radiation is extremely right-skewed, with most observations clustered near low values and a long tail extending toward high irradiance, reflecting seasonal and diurnal solar patterns and highlighting periods when photovoltaic generation may significantly affect net load and headroom. Air temperature shows a bimodal to moderately right-skewed distribution, with a dense concentration between roughly 45°F and 65°F and a tapering tail toward higher temperatures above 90°F; these extreme heat conditions are particularly important because they coincide with peak cooling demand and elevated congestion risk. Finally, the residential customer count distribution is heavily right-skewed, indicating that most feeders serve relatively small residential populations, while a small number of feeders support very large customer bases and therefore face structurally higher baseline demand. Together, these distributions demonstrate strong non-normality and wide variability across operating conditions, helping explain why nonlinear models perform well and why congestion risk is concentrated among a limited subset of high-load, high-customer-density feeders." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "features_to_plot = [\n", " 'load',\n", " 'Sol Rad (Ly/day)',\n", " 'Air Temp (F)',\n", " 'residential customer count'\n", "]\n", "\n", "fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n", "axes = axes.ravel()\n", "\n", "for idx, feature in enumerate(features_to_plot):\n", " if feature in X.columns:\n", " sns.histplot(X[feature], kde=True, ax=axes[idx])\n", " axes[idx].set_title(f'Distribution of {feature}')\n", " axes[idx].set_xlabel('')\n", " else:\n", " axes[idx].set_visible(False)\n", " print(f\"Warning: feature '{feature}' not found in X.columns\")\n", "\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The histogram of feeder headroom (mean_ica_sg) shows a strongly right-skewed distribution measured in kilowatts (kW). Most feeder–hour observations are concentrated at relatively low to moderate headroom levels, largely below about 5,000 kW, with the highest density occurring at the lower end of the range. As headroom increases, the frequency of observations declines steadily, forming a long right tail that extends beyond 15,000 kW. This distribution directly motivates our low–high congestion risk cutoff, as a substantial share of feeders operate within the lower headroom range where congestion is more likely. In contrast, only a small subset of feeders exhibit very high available capacity, indicating that elevated headroom is relatively rare across the system." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = pd.read_csv(\"outputs/feeder_features_cleaned.csv\")\n", "\n", "plt.figure(figsize=(6,4))\n", "plt.hist(df[\"mean_ica_sg\"].dropna(), bins=50)\n", "plt.xlabel(\"Mean ICA (MW Headroom)\")\n", "plt.ylabel(\"Count\")\n", "plt.title(\"Distribution of Feeder Headroom (mean_ica_sg)\")\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This figure shows feeder-level congestion rates clustered by ZIP code, with ZIP codes ordered from lowest to highest average congestion. Each dot represents a single feeder, and color intensity reflects congestion severity (blue = low, red = high). The plot reveals that most ZIP codes on the left side are dominated by feeders with near-zero congestion, indicating that congestion is not widespread across the system. In contrast, a small group of ZIP codes on the right exhibits a dense concentration of orange and red points, showing that many feeders in these areas are congested for a large fraction of observed month–hours.\n", "\n", "The dashed horizontal line at 50% of hours congested highlights a clear threshold separating moderate from severe congestion. Several ZIP codes contain multiple feeders that lie above this threshold, indicating persistent structural congestion rather than isolated or temporary overloads. At the same time, even within high-risk ZIP codes, there remains noticeable variability across feeders, suggesting that local feeder characteristics still play an important role beyond geographic location alone.\n", "\n", "Overall, the figure demonstrates that congestion risk is highly geographically concentrated, with a small subset of ZIP codes accounting for most chronic congestion. This supports a targeted, ZIP-specific and feeder-specific resource allocation strategy, rather than broad system-wide upgrades, and helps identify where engineering review and capital investment would be most impactful." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "df[\"congested\"] = (df[\"mean_ica_sg\"] < 1).astype(int)\n", "\n", "feeder_stats = (\n", " df.groupby([\"feederid\", \"ZIP_CODE\"])[\"congested\"]\n", " .agg([\"sum\", \"count\"])\n", " .reset_index()\n", ")\n", "feeder_stats[\"congestion_rate\"] = feeder_stats[\"sum\"] / feeder_stats[\"count\"]\n", "feeder_stats[\"ZIP_CODE\"] = feeder_stats[\"ZIP_CODE\"].astype(str)\n", "\n", "# Sort ZIPs by average congestion rate\n", "zip_avg = (\n", " feeder_stats.groupby(\"ZIP_CODE\")[\"congestion_rate\"]\n", " .mean()\n", " .sort_values()\n", ")\n", "sorted_zips = zip_avg.index.tolist()\n", "zip_map = {z: i for i, z in enumerate(sorted_zips)}\n", "\n", "feeder_stats[\"zip_x_base\"] = feeder_stats[\"ZIP_CODE\"].map(zip_map)\n", "\n", "np.random.seed(42) \n", "jitter = np.random.uniform(-0.25, 0.25, size=len(feeder_stats))\n", "feeder_stats[\"zip_x\"] = feeder_stats[\"zip_x_base\"] + jitter\n", "\n", "plt.figure(figsize=(14, 7))\n", "\n", "sc = plt.scatter(\n", " feeder_stats[\"zip_x\"],\n", " feeder_stats[\"congestion_rate\"],\n", " s=9,\n", " c=feeder_stats[\"congestion_rate\"],\n", " cmap=\"coolwarm\", # blue = low, red = high\n", " alpha=0.7\n", ")\n", "\n", "plt.axhline(0.5, linestyle=\"--\", linewidth=1)\n", "plt.text(0.5, 0.52, \"50% of hours congested\", fontsize=10)\n", "\n", "plt.xlabel(\"ZIP Code (sorted by average congestion)\")\n", "plt.ylabel(\"Congestion rate (share of month–hours with mean_ica_sg < 1 kW)\")\n", "plt.title(\"Feeder Congestion Rate by ZIP Code (Blue = Low, Red = High)\")\n", "plt.ylim(0, 1.05)\n", "\n", "step = max(1, len(sorted_zips) // 15)\n", "tick_positions = list(range(0, len(sorted_zips), step))\n", "tick_labels = [sorted_zips[i] for i in tick_positions]\n", "plt.xticks(tick_positions, tick_labels, rotation=45)\n", "\n", "cbar = plt.colorbar(sc)\n", "cbar.set_label(\"Congestion rate\")\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "the final feeder_features data frame shows 287424 NA values originating from PG&E's ICA data, which represents only 7% of the total data. These will be dropped when building models but since this is a small\n", "proportion of data, the merged data and merge pipeline frame appears to be structurally sound. Moreover, a histogram of feeder congestion reveals a left-skewed distribution with enough variation to proceed. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "=== feeder_features summary ===\n", "Shape (rows, columns): (287424, 25)\n", "Columns: ['feederid', 'month', 'hour', 'load', 'ZIP_CODE', '2023', '2024', 'YoY Change', '%change', 'loadorgen', 'mean_ica_sg', 'nominal voltage (kv)', 'redacted data', 'residential customer count', 'commercial customer count', 'industrial customer count', 'agricultural customer count', 'other customers', 'existing distributed generation (kw)', 'queued distributed generation (kw)', 'total distributed generation (kw)', 'voltage', 'Sol Rad (Ly/day)', 'Air Temp (F)', 'Wind Speed (mph)']\n", "\n", "Head of final dataframe:\n" ] }, { "data": { "text/html": [ "
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feederidmonthhourloadZIP_CODE20232024YoY Change%changeloadorgen...industrial customer countagricultural customer countother customersexisting distributed generation (kw)queued distributed generation (kw)total distributed generation (kw)voltageSol Rad (Ly/day)Air Temp (F)Wind Speed (mph)
01020411031.00.034.14793295928610.0727.0117.00.191803G...16229.06030420010230120.047.4393.852
11020411031.01.032.08356595928610.0727.0117.00.191803G...16229.06030420010230120.047.1743.587
21020411031.02.030.88502995928610.0727.0117.00.191803G...16229.06030420010230120.046.9193.945
31020411031.03.030.65882495928610.0727.0117.00.191803G...16229.06030420010230120.046.7554.213
41020411031.04.031.62599495928610.0727.0117.00.191803G...16229.06030420010230120.046.7264.129
\n", "

5 rows × 25 columns

\n", "
" ], "text/plain": [ " feederid month hour load ZIP_CODE 2023 2024 YoY Change \\\n", "0 102041103 1.0 0.0 34.147932 95928 610.0 727.0 117.0 \n", "1 102041103 1.0 1.0 32.083565 95928 610.0 727.0 117.0 \n", "2 102041103 1.0 2.0 30.885029 95928 610.0 727.0 117.0 \n", "3 102041103 1.0 3.0 30.658824 95928 610.0 727.0 117.0 \n", "4 102041103 1.0 4.0 31.625994 95928 610.0 727.0 117.0 \n", "\n", " %change loadorgen ... industrial customer count \\\n", "0 0.191803 G ... 162 \n", "1 0.191803 G ... 162 \n", "2 0.191803 G ... 162 \n", "3 0.191803 G ... 162 \n", "4 0.191803 G ... 162 \n", "\n", " agricultural customer count other customers \\\n", "0 2 9.0 \n", "1 2 9.0 \n", "2 2 9.0 \n", "3 2 9.0 \n", "4 2 9.0 \n", "\n", " existing distributed generation (kw) queued distributed generation (kw) \\\n", "0 6030 4200 \n", "1 6030 4200 \n", "2 6030 4200 \n", "3 6030 4200 \n", "4 6030 4200 \n", "\n", " total distributed generation (kw) voltage Sol Rad (Ly/day) Air Temp (F) \\\n", "0 10230 12 0.0 47.439 \n", "1 10230 12 0.0 47.174 \n", "2 10230 12 0.0 46.919 \n", "3 10230 12 0.0 46.755 \n", "4 10230 12 0.0 46.726 \n", "\n", " Wind Speed (mph) \n", "0 3.852 \n", "1 3.587 \n", "2 3.945 \n", "3 4.213 \n", "4 4.129 \n", "\n", "[5 rows x 25 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Info:\n", "\n", "RangeIndex: 287424 entries, 0 to 287423\n", "Data columns (total 25 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 feederid 287424 non-null object \n", " 1 month 287424 non-null float64\n", " 2 hour 287424 non-null float64\n", " 3 load 287424 non-null float64\n", " 4 ZIP_CODE 287424 non-null object \n", " 5 2023 285696 non-null float64\n", " 6 2024 285984 non-null float64\n", " 7 YoY Change 286272 non-null float64\n", " 8 %change 285696 non-null float64\n", " 9 loadorgen 266258 non-null object \n", " 10 mean_ica_sg 266258 non-null float64\n", " 11 nominal voltage (kv) 287424 non-null object \n", " 12 redacted data 287424 non-null object \n", " 13 residential customer count 287424 non-null int64 \n", " 14 commercial customer count 287424 non-null int64 \n", " 15 industrial customer count 287424 non-null int64 \n", " 16 agricultural customer count 287424 non-null int64 \n", " 17 other customers 258336 non-null float64\n", " 18 existing distributed generation (kw) 287424 non-null int64 \n", " 19 queued distributed generation (kw) 287424 non-null int64 \n", " 20 total distributed generation (kw) 287424 non-null int64 \n", " 21 voltage 287424 non-null int64 \n", " 22 Sol Rad (Ly/day) 286209 non-null float64\n", " 23 Air Temp (F) 285201 non-null float64\n", " 24 Wind Speed (mph) 286209 non-null float64\n", "dtypes: float64(12), int64(8), object(5)\n", "memory usage: 54.8+ MB\n", "\n", "loadorgen value counts:\n", "loadorgen\n", "G 266258\n", "NaN 21166\n", "Name: count, dtype: int64\n", "\n", "mean_ica_sg summary stats:\n", "count 266258.000000\n", "mean 2803.914609\n", "std 2243.752715\n", "min 0.000000\n", "25% 1022.000000\n", "50% 2368.801468\n", "75% 4249.636323\n", "max 17363.409091\n", "Name: mean_ica_sg, dtype: float64\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Unique months in final data: [np.float64(1.0), np.float64(2.0), np.float64(3.0), np.float64(4.0), np.float64(5.0), np.float64(6.0), np.float64(7.0), np.float64(8.0), np.float64(9.0), np.float64(10.0), np.float64(11.0), np.float64(12.0)]\n", "Unique hours in final data: [np.float64(0.0), np.float64(1.0), np.float64(2.0), np.float64(3.0), np.float64(4.0), np.float64(5.0), np.float64(6.0), np.float64(7.0), np.float64(8.0), np.float64(9.0), np.float64(10.0), np.float64(11.0), np.float64(12.0), np.float64(13.0), np.float64(14.0), np.float64(15.0), np.float64(16.0), np.float64(17.0), np.float64(18.0), np.float64(19.0), np.float64(20.0), np.float64(21.0), np.float64(22.0), np.float64(23.0)]\n" ] } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "\n", "# check feeder_features structure\n", "print(\"\\n=== feeder_features summary ===\")\n", "print(\"Shape (rows, columns):\", feeder_features.shape)\n", "print(\"Columns:\", feeder_features.columns.tolist())\n", "\n", "print(\"\\nHead of final dataframe:\")\n", "display(feeder_features.head())\n", "\n", "print(\"\\nInfo:\")\n", "feeder_features.info()\n", "\n", "# check loadorgen counts\n", "if \"loadorgen\" in feeder_features.columns:\n", " print(\"\\nloadorgen value counts:\")\n", " print(feeder_features[\"loadorgen\"].value_counts(dropna=False))\n", "\n", "if \"mean_ica_sg\" in feeder_features.columns:\n", " print(\"\\nmean_ica_sg summary stats:\")\n", " print(feeder_features[\"mean_ica_sg\"].describe())\n", "\n", "\n", "# check for month/hour coverage\n", "if {\"month\", \"hour\"}.issubset(feeder_features.columns):\n", " print(\"\\nUnique months in final data:\", sorted(feeder_features[\"month\"].dropna().unique()))\n", " print(\"Unique hours in final data:\", sorted(feeder_features[\"hour\"].dropna().unique()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Checking spatial coverage of feeder_features data fram\n", "The maps generated below show full coverage of PG&E feeder IDs from the feeder shapefiles, compared with the feeders in our final feeder_features data frame. Comparing te number of feeders in PG&E shapefiles with the feeders present in feeder_features reveals that abbroximately 2/3 of feeders are not in the final data frame. This is likely due to rural feeders not falling within California regions with Zip codes or due to the feeders not falling within one of the climate zones used in the CALMAC load profiles data. Despite this, more than 900 unique feeders are in the final data frame, which will provide enough data to model.We also constructed a heatmap which shows the distribution of feeder congestion across remaining feeders, as calculated by mean_ica_sg. this maps reveals ample spatial distribution of feeders and congestion within the feeder_features data frame, with plenty of distribution across climate zone." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feeder geometries in shapefile: 3023\n", "Feeders in feeder_features: 989\n", "Feeders with both geometry and ICA: 930\n" ] }, { "data": { "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "\n", "# Check coverage of resulting feeders and distribution of mean_ica_sg (congestion)\n", "# Load feeder shapefile\n", "feeders = gpd.read_file(\"ica_data/FeederDetail_Voltage.shp\")\n", "feeders[\"feederid\"] = normalize_feederid(feeders[\"feederid\"])\n", "\n", "# aggregate congestion accross each feeder\n", "if \"mean_ica_sg\" in feeder_features.columns:\n", " ff_unique = (\n", " feeder_features\n", " .groupby(\"feederid\", as_index=False)[\"mean_ica_sg\"]\n", " .mean()\n", " )\n", "else:\n", " raise ValueError(\"mean_ica_sg not found in feeder_features; cannot plot ICA heatmap.\")\n", "\n", "# Merge geometries with ICA summary\n", "feeders_merged = feeders.merge(ff_unique, on=\"feederid\", how=\"left\")\n", "\n", "# Coverage diagnostics\n", "n_geom = feeders[\"feederid\"].nunique()\n", "n_ff = feeder_features[\"feederid\"].nunique()\n", "n_with_ica_geom = feeders_merged.loc[\n", " feeders_merged[\"mean_ica_sg\"].notna(), \"feederid\"\n", "].nunique()\n", "\n", "print(f\"Feeder geometries in shapefile: {n_geom}\")\n", "print(f\"Feeders in feeder_features: {n_ff}\")\n", "print(f\"Feeders with both geometry and ICA: {n_with_ica_geom}\")\n", "\n", "# Map all feeders\n", "fig, ax = plt.subplots(figsize=(8, 10))\n", "feeders.plot(ax=ax, color=\"lightgray\", linewidth=0.4)\n", "ax.set_title(\"All PG&E Feeders (Shapefile)\")\n", "ax.set_axis_off()\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "# heat-map congestion\n", "fig, ax = plt.subplots(figsize=(8, 10))\n", "feeders_merged.plot(\n", " ax=ax,\n", " column=\"mean_ica_sg\",\n", " cmap=\"plasma\",\n", " legend=True,\n", " legend_kwds={\"label\": \"Average Generation Headroom (mean_ica_sg)\"},\n", " linewidth=0.4,\n", " missing_kwds={\"color\": \"lightgray\", \"label\": \"No ICA data\"},\n", ")\n", "ax.set_title(\"Feeders Colored by Average ICA (Generation Headroom)\")\n", "ax.set_axis_off()\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "EV adoption increased across most of California from 2023 to 2024. Statewide, 80% of ZIP codes saw positive EV growth, while only about 6% showed declines. When weighted by the size of each ZIP’s existing EV population, California experienced a 25% overall increase in EV adoption year-over-year. The absolute EV counts reinforce this trend, showing that 2024 levels exceed 2023 nearly everywhere, while a handful of dense ZIPs still dominate total volumes.\n", "Although our selected feeders represent only a small subset of these ZIPs, analyzing the full statewide distribution is critical. It reveals how broad and diffuse EV growth has become, highlights the tail-risk of rapid adoption pockets, and demonstrates why our modeling methodology must be robust at both the local and statewide scale. This broader context strengthens the generalizability and long-term relevance of our approach. See below for two charts demonstrating this. \n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average YoY Growth: 40.98%\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from matplotlib.ticker import FuncFormatter\n", "\n", "# 1. Load data\n", "df = pd.read_csv(\"EV_Pop_Growth_23_24.csv\")\n", "\n", "# 2. Keep only positive YoY growth\n", "df_filtered = df[df[\"%change\"] > 0].copy()\n", "\n", "# 3. Convert fractional YoY values to percent\n", "df_filtered[\"YoY_percent\"] = df_filtered[\"%change\"] * 100\n", "\n", "# 4. Compute the mean YoY growth (%)\n", "mean_growth = df_filtered[\"YoY_percent\"].mean()\n", "\n", "print(f\"Average YoY Growth: {mean_growth:.2f}%\") \n", "\n", "# 5. Percentage formatter for log-scale y-axis\n", "def percent_formatter(x, pos):\n", " return f\"{x:.0f}%\"\n", "\n", "formatter = FuncFormatter(percent_formatter)\n", "\n", "# 6. Plot\n", "plt.figure(figsize=(10,6))\n", "\n", "plt.scatter(\n", " df_filtered[\"Zip Code\"],\n", " df_filtered[\"YoY_percent\"],\n", " s=12,\n", " alpha=0.7,\n", " label=\"ZIP-level YoY Growth\"\n", ")\n", "\n", "plt.yscale(\"log\")\n", "plt.gca().yaxis.set_major_formatter(formatter)\n", "\n", "# --- Add the average line ---\n", "plt.axhline(\n", " mean_growth,\n", " color=\"red\",\n", " linestyle=\"-\",\n", " linewidth=2,\n", " alpha=0.8,\n", " label=f\"Average Growth ({mean_growth:.1f}%)\"\n", ")\n", "\n", "# Optional reference gridlines\n", "for ref in [10, 20, 50, 100, 200, 500, 1000]:\n", " plt.axhline(ref, linestyle=\"-\", linewidth=0.6, alpha=0.3)\n", "\n", "plt.xlabel(\"ZIP Code\")\n", "plt.ylabel(\"YoY EV Growth (%) — log scale\")\n", "plt.title(\"EV Year-over-Year Growth by ZIP (2023 → 2024)\\nWith Average YoY Growth Line\")\n", "\n", "plt.legend()\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average 2023 EVs per ZIP: 659.3\n", "Average 2024 EVs per ZIP: 810.0\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "# 1. Load data\n", "df = pd.read_csv(\"EV_Pop_Growth_23_24.csv\") \n", "\n", "# 2. Extract columns\n", "zips = df[\"Zip Code\"]\n", "ev_2023 = df[\"2023\"]\n", "ev_2024 = df[\"2024\"]\n", "\n", "# 3. Compute means\n", "mean_2023 = ev_2023.mean()\n", "mean_2024 = ev_2024.mean()\n", "\n", "print(f\"Average 2023 EVs per ZIP: {mean_2023:.1f}\")\n", "print(f\"Average 2024 EVs per ZIP: {mean_2024:.1f}\")\n", "\n", "# 4. Plot (with log scale)\n", "plt.figure(figsize=(10, 6))\n", "\n", "# Scatter: EV counts\n", "plt.scatter(\n", " zips,\n", " ev_2023,\n", " s=12,\n", " alpha=0.6,\n", " color=\"tab:orange\",\n", " label=\"2023 EV count\"\n", ")\n", "\n", "# Scatter: 2024 EV counts\n", "plt.scatter(\n", " zips,\n", " ev_2024,\n", " s=12,\n", " alpha=0.6,\n", " color=\"tab:blue\",\n", " label=\"2024 EV count\"\n", ")\n", "\n", "# 5. Add average lines \n", "plt.axhline(\n", " mean_2023,\n", " color=\"tab:orange\",\n", " linestyle=\"--\",\n", " linewidth=1.2,\n", " label=f\"2023 mean ({mean_2023:.0f} EVs)\"\n", ")\n", "\n", "plt.axhline(\n", " mean_2024,\n", " color=\"tab:blue\",\n", " linestyle=\"--\",\n", " linewidth=1.2,\n", " label=f\"2024 mean ({mean_2024:.0f} EVs)\"\n", ")\n", "\n", "# 6. Log scale for Y axis\n", "plt.yscale(\"log\")\n", "\n", "plt.xlabel(\"ZIP Code\")\n", "plt.ylabel(\"EV Count (log scale)\")\n", "plt.title(\"EV Counts by ZIP (2023 vs 2024)\\nWith Mean Lines — Log Scale\")\n", "plt.legend()\n", "plt.tight_layout()\n", "plt.show()\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Forecasting and Prediction Modeling (25 points)\n", "\n", "This section is where the rubber meets the road. In it you must:\n", "1. Explore at least 3 prediction modeling approaches for each prediction question, ranging from the simple (e.g. linear regression, KNN) to the complex (e.g. SVM, random forests, Lasso). \n", "2. Motivate all your modeling decisions. This includes parameter choices (e.g., how many folds in k-fold cross validation, what time window you use for averaging your data) as well as model form (e.g., If you use regression trees, why? If you include nonlinear features in a regression model, why?). \n", "1. Carefully describe your cross validation and model selection process. You should partition your data into training and testing data sets. The training data set is what you use for cross-validation (i.e. you sample from within it to create folds, etc.). The testing data set is held to the very end of your efforts, and used to compare qualitatively different models (e.g. OLS vs random forests).\n", "4. Very carefully document your workflow. We will be reading a lot of projects, so we need you to explain each basic step in your analysis. \n", "5. Seek opportunities to write functions allow you to avoid doing things over and over, and that make your code more succinct and readable. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Prediction model 1: Regression:\n", "\n", "##### Motivation and Model Selection:\n", "\n", "For this prediction problem, we frame feeder congestion as a continuous regression task at the feeder–month–hour level. The target variable is the available headroom in MW (mean_ica_sg), interpreted as the remaining solar hosting capacity on each feeder at a given month–hour. Our feature set combines relatively static feeder attributes (e.g., voltage class, existing distributed generation, customer mix) with more dynamic temporal and environmental variables (month, hour, system load, and weather). The core goal is to estimate “How much headroom will this feeder have at month–hour?”, so that planners can identify feeders where capacity is tightening over time and evaluate the impacts of future DER or load-growth scenarios.\n", "\n", "We explore three main families of regression models that span the complexity spectrum: OLS as a simple linear baseline, regularized linear models (Ridge and Lasso regression), and a Random Forest regressor as a flexible, non-linear ensemble model. To evaluate and select models, we partitioned the data into a training set and a held-out test set using an 80/20 split. The training set is used exclusively for model fitting and cross-validation, if that was included on the model, while the test set is held out until the very end for a one-time comparison of final model performance. All linear models (OLS, Ridge, Lasso) are standardized using StandardScaler so that features are on comparable scales. This is especially important for Ridge and Lasso, whose penalties depend on the scale of the coefficients. The Random Forest model does not use standardization, because tree-based models are inherently scale-invariant.\n", "\n", "Within the training data for Ridge, Lasso, and Random Forest models, we use k-fold cross-validation (10-folds) to tune hyperparameters and estimate out-of-sample error. For Ridge and Lasso, we cross-validated to select the α that minimizes the average Root Mean Squared Error (RMSE) across folds. For the Random Forest, we tuned a small set of key hyperparameters, including the number of trees and maximum depth, to balance predictive performance against training time. Our primary selection metric is RMSE, since it has the same units as the target (MW headroom) and penalizes larger errors more strongly, although we also report MAE and R² to provide additional context.\n", "\n", "Before fitting models, we performed basic feature screening to improve the potential predictive power of models generated. We restricted the predictor set to numeric variables (excluding the target), identified any strictly categorical columns for potential encoding or exclusion, and then computed pairwise correlations and Variance Inflation Factors (VIF) to flag highly collinear predictors. Predictors with extremely high correlation or VIF values are considered for removal in order to reduce instability in OLS and to make the Ridge and Lasso penalties more meaningful. This preprocessing step is implemented in a short diagnostic script that prints detected categorical variables and VIF-based drop candidates.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape: (266258, 25)\n", "Columns: ['feederid', 'month', 'hour', 'load', 'ZIP_CODE', '2023', '2024', 'YoY Change', '%change', 'loadorgen', 'mean_ica_sg', 'Sol Rad (Ly/day)', 'Air Temp (F)', 'Wind Speed (mph)', 'nominal voltage (kv)', 'redacted data', 'residential customer count', 'commercial customer count', 'industrial customer count', 'agricultural customer count', 'other customers', 'existing distributed generation (kw)', 'queued distributed generation (kw)', 'total distributed generation (kw)', 'voltage']\n", "Shape after dropping NaNs: (235379, 25)\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Categorical variables:\n", "['loadorgen', 'nominal voltage (kv)', 'redacted data']\n", "\n", "Numeric variables:\n", "['feederid', 'month', 'hour', 'load', 'ZIP_CODE', '2023', '2024', 'YoY Change', '%change', 'Sol Rad (Ly/day)', 'Air Temp (F)', 'Wind Speed (mph)', 'residential customer count', 'commercial customer count', 'industrial customer count', 'agricultural customer count', 'other customers', 'existing distributed generation (kw)', 'queued distributed generation (kw)', 'total distributed generation (kw)', 'voltage']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/statsmodels/stats/outliers_influence.py:197: RuntimeWarning: divide by zero encountered in scalar divide\n", " vif = 1. / (1. - r_squared_i)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "VIF scores:\n", " feature VIF\n", "17 existing distributed generation (kw) inf\n", "19 total distributed generation (kw) inf\n", "5 2023 inf\n", "6 2024 inf\n", "7 YoY Change inf\n", "18 queued distributed generation (kw) inf\n", "4 ZIP_CODE 59.738039\n", "9 Sol Rad (Ly/day) 8.843746\n", "3 load 5.471105\n", "0 feederid 4.994572\n", "10 Air Temp (F) 3.812297\n", "13 commercial customer count 3.343842\n", "14 industrial customer count 2.953522\n", "12 residential customer count 2.255829\n", "20 voltage 1.685752\n", "11 Wind Speed (mph) 1.650039\n", "16 other customers 1.569475\n", "8 %change 1.428101\n", "15 agricultural customer count 1.364005\n", "2 hour 1.273723\n", "1 month 1.077896\n", "\n", "Suggested drops from VIF > 10:\n", "['existing distributed generation (kw)', 'total distributed generation (kw)', '2023', '2024', 'YoY Change', 'queued distributed generation (kw)', 'ZIP_CODE']\n", "\n", "--- Summary ---\n", "Categorical variables: ['loadorgen', 'nominal voltage (kv)', 'redacted data']\n", "VIF-based drop candidates: ['existing distributed generation (kw)', 'total distributed generation (kw)', '2023', '2024', 'YoY Change', 'queued distributed generation (kw)', 'ZIP_CODE']\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from statsmodels.stats.outliers_influence import variance_inflation_factor\n", "\n", "\n", "# load data\n", "DATA_PATH = \"/Users/phealy92/CascadeProjects/Predicted-Feeder-Congestion/outputs/feeder_features_cleaned.csv\"\n", "TARGET_COL = \"mean_ica_sg\"\n", "\n", "\n", "df = pd.read_csv(DATA_PATH)\n", "print(f\"Shape: {df.shape}\")\n", "print(\"Columns:\", df.columns.tolist())\n", "\n", "\n", "# drop rows with missing values (diagnostics only)\n", "df_clean = df.dropna()\n", "print(\"Shape after dropping NaNs:\", df_clean.shape)\n", "\n", "\n", "# histogram of target variable distribution\n", "plt.hist(df_clean[TARGET_COL], bins=30, edgecolor='black')\n", "plt.title(\"Distribution of Target Variable\")\n", "plt.xlabel(TARGET_COL)\n", "plt.ylabel(\"Frequency\")\n", "plt.show()\n", "\n", "\n", "# identify categorical variables\n", "categorical_vars = df_clean.select_dtypes(include=[\"object\", \"category\"]).columns.tolist()\n", "print(\"\\nCategorical variables:\")\n", "print(categorical_vars if categorical_vars else \"None\")\n", "\n", "\n", "# identify numeric variables (exclude target)\n", "numeric_cols = df_clean.select_dtypes(include=[np.number]).columns.tolist()\n", "numeric_cols = [c for c in numeric_cols if c != TARGET_COL]\n", "print(\"\\nNumeric variables:\")\n", "print(numeric_cols)\n", "\n", "\n", "# VIF collinearity check\n", "X_num = df_clean[numeric_cols].astype(float)\n", "vif_data = []\n", "\n", "\n", "for i, col in enumerate(numeric_cols):\n", " vif_val = variance_inflation_factor(X_num.values, i)\n", " vif_data.append((col, vif_val))\n", "\n", "\n", "vif_df = pd.DataFrame(vif_data, columns=[\"feature\", \"VIF\"]).sort_values(\"VIF\", ascending=False)\n", "print(\"\\nVIF scores:\")\n", "print(vif_df)\n", "\n", "\n", "vif_threshold = 10\n", "vif_drop_candidates = vif_df.loc[vif_df[\"VIF\"] > vif_threshold, \"feature\"].tolist()\n", "\n", "\n", "print(f\"\\nSuggested drops from VIF > {vif_threshold}:\")\n", "print(vif_drop_candidates if vif_drop_candidates else \"None\")\n", "\n", "\n", "# summary\n", "print(\"\\nSummary\")\n", "print(\"Categorical variables:\", categorical_vars)\n", "print(\"VIF-based drop candidates:\", vif_drop_candidates if vif_drop_candidates else [])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From this output, we noticed that columns for '2023', '2024', and 'YoY Change' were perfectly collinear, so we decided to remove the ‘2023’ and ‘2024’ columns. We also noticed that 'existing distributed generation (kw)', 'queued distributed generation (kw)', and 'total distributed generation (kw)' were perfectly collinear, and because we were using static data, we decided to use just ‘existing distributed generation (kw)' and remove 'queued distributed generation (kw)', and 'total distributed generation (kw)' columns. Columns ‘loadorgen’ and ‘redacted data’ were identified as categorical features that served little predictive purpose, so they were removed. It was recognized that the feature column ‘nominal voltage’ was redundant with ‘voltage’, so nominal voltage was removed. Finally, ‘ZIP_CODE’ had a high VIF, and after discussing with the team, it was decided that this feature had little contribution to the model, as the geographic location of a feature probably does not contribute predictive power in a model with weather data and load shapes.\n", "\n", "OLS provides a transparent starting point that assumes a linear relationship between headroom and the predictors and is easy to interpret in terms of marginal effects. Each coefficient represents the expected change in ICA headroom for a one-unit change in the corresponding feature, holding all other features constant. This makes OLS especially useful for initial model exploration, diagnosing multicollinearity, and identifying which operational or weather variables are most strongly associated with feeder congestion. However, OLS also relies on assumptions such as linearity, homoscedasticity, and lack of strong collinearity; violations of these assumptions may lead to biased standard errors or unstable coefficient estimates. As a result, while OLS offers interpretability and serves as a useful baseline model, it may need to be supplemented with regularization, nonlinear models, or feature engineering to better capture the complex dynamics driving ICA headroom. \n", "Three linear models (ordinary least squares, Ridge, and Lasso regression) produced nearly identical performance, with RMSE values around 1501 MW, MAE values near 1124 MW, and an R² of approximately 0.56, showing that linear relationships explain only about half of the variability in mean MW headroom. In contrast, the Random Forest model performed substantially better, achieving an RMSE of 240 MW, an MAE of 106 MW, and an R² of 0.989. This large improvement indicates that mean feeder headroom is governed by strong nonlinear relationships among load, time, weather, customer composition, and distributed generation, which are effectively captured by the Random Forest but not by linear models." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using numeric feature columns:\n", "['feederid', 'month', 'hour', 'load', '2024', 'Sol Rad (Ly/day)', 'Air Temp (F)', 'Wind Speed (mph)', 'residential customer count', 'commercial customer count', 'industrial customer count', 'agricultural customer count', 'other customers', 'existing distributed generation (kw)', 'queued distributed generation (kw)', 'total distributed generation (kw)', 'voltage']\n", "\n", "OLS Regression Results\n", "RMSE: 1500.958\n", "R²: 0.556\n", "\n", "Coefficients\n", " feature coefficient\n", "16 voltage 253.929570\n", "10 industrial customer count 13.411032\n", "3 load 10.937584\n", "6 Air Temp (F) 9.402767\n", "7 Wind Speed (mph) 8.276348\n", "2 hour 2.358447\n", "12 other customers 0.662437\n", "4 2024 0.263175\n", "8 residential customer count 0.151077\n", "5 Sol Rad (Ly/day) 0.069353\n", "13 existing distributed generation (kw) 0.017328\n", "0 feederid -0.000002\n", "15 total distributed generation (kw) -0.047461\n", "14 queued distributed generation (kw) -0.064789\n", "9 commercial customer count -4.009078\n", "1 month -4.237458\n", "11 agricultural customer count -5.340179\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.metrics import mean_squared_error, r2_score\n", "\n", "\n", "# load dataset and define target feature\n", "DATA_PATH = \"/Users/phealy92/CascadeProjects/Predicted-Feeder-Congestion/outputs/feeder_features_cleaned_PhillipEdits_v2.csv\"\n", "TARGET_COL = \"mean_ica_sg\"\n", "\n", "\n", "df = pd.read_csv(DATA_PATH)\n", "\n", "\n", "# define feature columns as all numeric columns except the target\n", "feature_cols = [\n", " col for col in df.select_dtypes(include=[np.number]).columns\n", " if col != TARGET_COL\n", "]\n", "\n", "\n", "print(\"Using numeric feature columns:\")\n", "print(feature_cols)\n", "\n", "\n", "# drop rows with missing values in features or target\n", "df_clean = df.dropna(subset=feature_cols + [TARGET_COL])\n", "\n", "\n", "# define feature matrix and target variable\n", "X = df_clean[feature_cols]\n", "y = df_clean[TARGET_COL]\n", "\n", "\n", "# train-test split\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X, y, test_size=0.2, random_state=42\n", ")\n", "\n", "\n", "# OLS Regression Model (no pipeline)\n", "model = LinearRegression()\n", "model.fit(X_train, y_train)\n", "\n", "\n", "# evaluate model on test set\n", "y_pred = model.predict(X_test)\n", "\n", "\n", "mse = mean_squared_error(y_test, y_pred)\n", "rmse = np.sqrt(mse)\n", "r2 = r2_score(y_test, y_pred)\n", "\n", "\n", "print(\"\\nOLS Regression Results\")\n", "print(f\"RMSE: {rmse:.3f}\")\n", "print(f\"R²: {r2:.3f}\")\n", "\n", "\n", "# extract coefficients (same order as feature_cols)\n", "coef = model.coef_\n", "\n", "\n", "coef_df = pd.DataFrame({\n", " \"feature\": feature_cols,\n", " \"coefficient\": coef\n", "})\n", "\n", "\n", "print(\"\\nCoefficients\")\n", "print(coef_df.sort_values(\"coefficient\", ascending=False))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To attempt to address this, we employed the use of regularization models: Ridge regression to stabilize coefficient estimates in the presence of collinearity and Lasso regression to perform implicit feature selection by shrinking some coefficients toward zero. \n", "\n", "During Lasso runs, scikit-learn raises “RuntimeWarning” messages about overflow and invalid values in matrix multiplication. This indicates that for some combinations of features and regularization strength, the optimization problem was numerically unstable: coefficients grew extremely large, leading to infinite or undefined predictions. This would suggest that predictions with lasso had low utility, and that Lasso is probably not an appropriate model for this dataframe." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using numeric feature columns:\n", "['feederid', 'month', 'hour', 'load', '2024', 'Sol Rad (Ly/day)', 'Air Temp (F)', 'Wind Speed (mph)', 'residential customer count', 'commercial customer count', 'industrial customer count', 'agricultural customer count', 'other customers', 'existing distributed generation (kw)', 'queued distributed generation (kw)', 'total distributed generation (kw)', 'voltage']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Best Ridge alpha: 26.366508987303554\n", "Ridge CV RMSE: 1493.069\n", "\n", "Ridge Test Results\n", "Test RMSE: 1500.956\n", "Test R²: 0.556\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Best Lasso alpha: 0.011288378916846888\n", "Lasso CV RMSE: 1493.069\n", "\n", "Lasso Test Results\n", "Test RMSE: 1500.957\n", "Test R²: 0.556\n", "\n", "Lasso Coefficients\n", " feature coefficient\n", "16 voltage 1155.760794\n", "10 industrial customer count 663.998213\n", "4 2024 462.239766\n", "3 load 299.522009\n", "8 residential customer count 215.534087\n", "6 Air Temp (F) 118.889078\n", "15 total distributed generation (kw) 41.776036\n", "5 Sol Rad (Ly/day) 40.594893\n", "2 hour 16.371529\n", "7 Wind Speed (mph) 15.181900\n", "12 other customers 6.064747\n", "1 month -14.608408\n", "13 existing distributed generation (kw) -133.693909\n", "0 feederid -195.041815\n", "14 queued distributed generation (kw) -257.951855\n", "11 agricultural customer count -441.713780\n", "9 commercial customer count -534.059261\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: divide by zero encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: overflow encountered in matmul\n", " return X @ coef_ + self.intercept_\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_base.py:279: RuntimeWarning: invalid value encountered in matmul\n", " return X @ coef_ + self.intercept_\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "\n", "from sklearn.model_selection import train_test_split, KFold, cross_val_score\n", "from sklearn.linear_model import Ridge, Lasso\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.metrics import mean_squared_error, r2_score\n", "\n", "\n", "# load dataset and define target feature\n", "DATA_PATH = \"/Users/phealy92/CascadeProjects/Predicted-Feeder-Congestion/outputs/feeder_features_cleaned_PhillipEdits_v2.csv\"\n", "TARGET_COL = \"mean_ica_sg\"\n", "\n", "\n", "df = pd.read_csv(DATA_PATH)\n", "\n", "\n", "# define feature columns as all numeric columns except the target\n", "feature_cols = [\n", " col for col in df.select_dtypes(include=[np.number]).columns\n", " if col != TARGET_COL\n", "]\n", "\n", "\n", "print(\"Using numeric feature columns:\")\n", "print(feature_cols)\n", "\n", "\n", "# drop rows with missing values in features or target\n", "df_clean = df.dropna(subset=feature_cols + [TARGET_COL])\n", "\n", "\n", "# define feature matrix and target variable\n", "X = df_clean[feature_cols]\n", "y = df_clean[TARGET_COL]\n", "\n", "\n", "# train-test split\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X, y, test_size=0.2, random_state=42\n", ")\n", "\n", "\n", "# scale features (critical for Ridge/Lasso stability)\n", "scaler = StandardScaler()\n", "X_train_scaled = scaler.fit_transform(X_train)\n", "X_test_scaled = scaler.transform(X_test)\n", "\n", "\n", "# sanity check for numerical issues after scaling\n", "assert np.isfinite(X_train_scaled).all(), \"Non-finite values in X_train_scaled\"\n", "assert np.isfinite(X_test_scaled).all(), \"Non-finite values in X_test_scaled\"\n", "\n", "\n", "# helper function to compute RMSE via k-fold cross-validation\n", "def cv_rmse(model, X, y, n_splits=5):\n", " kf = KFold(n_splits=n_splits, shuffle=True, random_state=42)\n", " neg_mse_scores = cross_val_score(\n", " model, X, y,\n", " scoring=\"neg_mean_squared_error\",\n", " cv=kf\n", " )\n", " mse_scores = -neg_mse_scores\n", " return np.sqrt(mse_scores.mean())\n", "\n", "\n", "# ridge regression with CV over alpha\n", "ridge_alphas = np.logspace(-3, 3, 20) # from 0.001 to 1000\n", "\n", "\n", "best_ridge_alpha = None\n", "best_ridge_rmse = np.inf\n", "\n", "\n", "for a in ridge_alphas:\n", " ridge = Ridge(alpha=a, max_iter=10000)\n", " rmse = cv_rmse(ridge, X_train_scaled, y_train)\n", " if rmse < best_ridge_rmse:\n", " best_ridge_rmse = rmse\n", " best_ridge_alpha = a\n", "\n", "\n", "print(f\"\\nBest Ridge alpha: {best_ridge_alpha}\")\n", "print(f\"Ridge CV RMSE: {best_ridge_rmse:.3f}\")\n", "\n", "\n", "# fit final ridge model on full training data\n", "best_ridge = Ridge(alpha=best_ridge_alpha, max_iter=10000)\n", "best_ridge.fit(X_train_scaled, y_train)\n", "\n", "\n", "# evaluate ridge on test set\n", "y_pred_ridge = best_ridge.predict(X_test_scaled)\n", "ridge_test_mse = mean_squared_error(y_test, y_pred_ridge)\n", "ridge_test_rmse = np.sqrt(ridge_test_mse)\n", "ridge_test_r2 = r2_score(y_test, y_pred_ridge)\n", "\n", "\n", "print(\"\\nRidge Test Results\")\n", "print(f\"Test RMSE: {ridge_test_rmse:.3f}\")\n", "print(f\"Test R²: {ridge_test_r2:.3f}\")\n", "\n", "\n", "# lasso regression with CV over alpha\n", "lasso_alphas = np.logspace(-3, 1, 20) # from 0.001 to 10\n", "\n", "\n", "best_lasso_alpha = None\n", "best_lasso_rmse = np.inf\n", "\n", "\n", "for a in lasso_alphas:\n", " lasso = Lasso(alpha=a, max_iter=10000)\n", " rmse = cv_rmse(lasso, X_train_scaled, y_train)\n", " if rmse < best_lasso_rmse:\n", " best_lasso_rmse = rmse\n", " best_lasso_alpha = a\n", "\n", "\n", "print(f\"\\nBest Lasso alpha: {best_lasso_alpha}\")\n", "print(f\"Lasso CV RMSE: {best_lasso_rmse:.3f}\")\n", "\n", "\n", "# fit final lasso model on full training data\n", "best_lasso = Lasso(alpha=best_lasso_alpha, max_iter=10000)\n", "best_lasso.fit(X_train_scaled, y_train)\n", "\n", "\n", "# evaluate lasso on test set\n", "y_pred_lasso = best_lasso.predict(X_test_scaled)\n", "lasso_test_mse = mean_squared_error(y_test, y_pred_lasso)\n", "lasso_test_rmse = np.sqrt(lasso_test_mse)\n", "lasso_test_r2 = r2_score(y_test, y_pred_lasso)\n", "\n", "\n", "print(\"\\nLasso Test Results\")\n", "print(f\"Test RMSE: {lasso_test_rmse:.3f}\")\n", "print(f\"Test R²: {lasso_test_r2:.3f}\")\n", "\n", "\n", "# lasso coefficients (on standardized features)\n", "lasso_coef = best_lasso.coef_\n", "coef_df = pd.DataFrame({\n", " \"feature\": feature_cols, # same order as columns in X\n", " \"coefficient\": lasso_coef\n", "})\n", "\n", "\n", "print(\"\\nLasso Coefficients\")\n", "print(coef_df.sort_values(\"coefficient\", ascending=False))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we include a Random Forest model to capture non-linearities and higher-order interactions (e.g., between load, customer mix, and weather) that are unlikely to be well-approximated by a purely linear model." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using numeric feature columns:\n", "['feederid', 'month', 'hour', 'load', '2024', 'Sol Rad (Ly/day)', 'Air Temp (F)', 'Wind Speed (mph)', 'residential customer count', 'commercial customer count', 'industrial customer count', 'agricultural customer count', 'other customers', 'existing distributed generation (kw)', 'queued distributed generation (kw)', 'total distributed generation (kw)', 'voltage']\n", "\n", "Random Forest 10-Fold CV on Training Set\n", "RMSE scores: [237.88893846 260.23150357 243.84574604 239.61444242 246.97824721\n", " 229.91397025 232.55531434 236.61959053 241.82737946 242.04366396]\n", "Mean RMSE: 241.152\n", "Std RMSE: 8.001\n", "\n", "Random Forest Test Results\n", "RMSE: 240.251\n", "R²: 0.989\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "from sklearn.model_selection import train_test_split, KFold, cross_val_score\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.metrics import mean_squared_error, r2_score\n", "\n", "\n", "# load dataset and define target feature\n", "DATA_PATH = \"/Users/phealy92/CascadeProjects/Predicted-Feeder-Congestion/outputs/feeder_features_cleaned_PhillipEdits_v2.csv\"\n", "TARGET_COL = \"mean_ica_sg\" # change to \"min_ica_sg\" if you want\n", "\n", "\n", "df = pd.read_csv(DATA_PATH)\n", "\n", "\n", "# use all numeric columns except the target as features\n", "feature_cols = [\n", " col for col in df.select_dtypes(include=[np.number]).columns\n", " if col != TARGET_COL\n", "]\n", "\n", "\n", "print(\"Using numeric feature columns:\")\n", "print(feature_cols)\n", "\n", "\n", "# drop rows with missing values in features or target\n", "df_clean = df.dropna(subset=feature_cols + [TARGET_COL])\n", "\n", "\n", "X = df_clean[feature_cols]\n", "y = df_clean[TARGET_COL]\n", "\n", "\n", "# train-test split\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X, y, test_size=0.2, random_state=42\n", ")\n", "\n", "\n", "# random forest model\n", "rf = RandomForestRegressor(\n", " n_estimators=300,\n", " max_depth=None,\n", " random_state=42,\n", " n_jobs=-1\n", ")\n", "\n", "\n", "# 10-fold cross-validation on the training set\n", "kfold = KFold(n_splits=10, shuffle=True, random_state=42)\n", "\n", "\n", "# scoring='neg_root_mean_squared_error'; negative RMSE, so take the negative\n", "cv_scores = cross_val_score(\n", " rf,\n", " X_train,\n", " y_train,\n", " cv=kfold,\n", " scoring=\"neg_root_mean_squared_error\",\n", " n_jobs=-1\n", ")\n", "\n", "\n", "cv_rmse = -cv_scores # make positive; cross_val_score returns negative values\n", "\n", "\n", "print(\"\\nRandom Forest 10-Fold CV on Training Set\")\n", "print(\"RMSE scores:\", cv_rmse)\n", "print(f\"Mean RMSE: {cv_rmse.mean():.3f}\")\n", "print(f\"Std RMSE: {cv_rmse.std():.3f}\")\n", "\n", "\n", "# fit model on training data\n", "rf.fit(X_train, y_train)\n", "\n", "\n", "y_pred = rf.predict(X_test)\n", "\n", "\n", "mse = mean_squared_error(y_test, y_pred)\n", "rmse = np.sqrt(mse)\n", "r2 = r2_score(y_test, y_pred)\n", "\n", "\n", "print(\"\\nRandom Forest Test Results\")\n", "print(f\"RMSE: {rmse:.3f}\")\n", "print(f\"R²: {r2:.3f}\")\n", "\n", "\n", "# evaluate feature importances\n", "importances = rf.feature_importances_\n", "\n", "\n", "feat_imp_df = pd.DataFrame({\n", " \"feature\": feature_cols,\n", " \"importance\": importances\n", "}).sort_values(\"importance\", ascending=False)\n", "\n", "\n", "# create feature importance plot\n", "plt.figure(figsize=(10, 6))\n", "feat_imp_df.sort_values('importance', ascending=True).plot.barh(\n", " x='feature',\n", " y='importance',\n", " color='skyblue',\n", " figsize=(10, 8)\n", ")\n", "\n", "\n", "# feature importance plot labels and title\n", "plt.title('Feature Importances in Random Forest Model', fontsize=14)\n", "plt.xlabel('Importance Score')\n", "plt.ylabel('Features')\n", "plt.tight_layout()\n", "\n", "\n", "# show the plot\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is a summary table of the RMSE and R2 values for each of the models:\n", "\n", "| Model | Test RMSE (kW) | Test R² |\n", "|----------------|----------------|---------|\n", "| OLS | 1518.6 | 0.545 |\n", "| Ridge Reg. | 1518.6 | 0.545 |\n", "| Lasso Reg. | 1518.6 | 0.545 |\n", "| RF Regression | 240.5 | 0.989 |\n", "\n", "The Random Forest model is the only model from the above four models tested that can predict feeder headroom while explaining variation. Using RF, we can predict feeder headroom at any month–hour with an average error of 240 kW. The other models have far less explanatory power and a RMSE that exceeds 1.5 MW, which is not useful since we later define feeder congestion as being < 1 MW of headroom. The RF regression model explains 98.9% of the variation in ICA headroom and therefore provides a reliable estimate for operational decision-making. Linear models (OLS, Ridge, Lasso) are not trustworthy for this task due to severe instability and poor performance.\n", "\n", "A final interesting note is that based on the feature analysis of the RF regression model, weather data seemed to have little influence on the model, suggesting that this could be left out in a more refined model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Prediction model 2: Binary Classification:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Motivation and Model Selection:\n", "\n", "To predict whether a feeder becomes congested (below 1 MW of headroom), we tested a mix of simple and complex models: Logistic Regression (two versions), Linear SVM, KNN, and Random Forest. We wanted to see how far simple linear models could go before switching to more flexible nonlinear approaches:\n", "\n", "Logistic Regression (Default, Ridge / L2):\n", "\n", "We used logistic regression as our starting baseline model because it is simple and easy to interpret. It allows us to clearly see how each feature is related to congestion risk, which makes it helpful for understanding general trends in the data.\n", "\n", "Logistic Regression (C = 10, Weaker Regularization):\n", "\n", "This version of logistic regression is a less constrained version of the baseline model. By allowing the model to fit the training data more closely, we tested whether reducing the regularization would improve predictions. \n", "\n", "Support Vector Machine (SVM)\n", "\n", "The SVM looks for the best separating boundary between congested and non-congested feeders by maximizing the margin between the two groups. \n", "\n", "Random Forest \n", "\n", "The Random Forest model combines many decision trees to make a final prediction. We included this model because feeder congestion is influenced by interacting physical and behavioral factors, which simple linear models cannot fully represent.\n", "\n", "K-Nearest Neighbors (KNN)\n", "\n", "The KNN model makes predictions by looking at the most similar past observations and using their outcomes to classify new cases. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Data Processing:\n", "\n", "Before training any models, we spent time preparing the data so that all models would be learning from clean, comparable, and reliable inputs. Since our dataset contains a mix of numerical and categorical features (such as weather variables, customer counts, and distributed generation), we first converted all categorical variables into numeric form using one-hot encoding. This step allows the models to interpret categories mathematically rather than as text.\n", "\n", "Next, we addressed missing values, which are unavoidable in real-world operational datasets. Instead of discarding large portions of the data, we used median imputation to fill in missing numerical values. \n", "\n", "After splitting the data into training (80%) and testing (20%) sets, we applied standardization only after the split to avoid information leakage. Using StandardScaler, we transformed each numerical feature so that it had a \"mean of 0 and a standard deviation of 1\", based solely on the training data. This step is especially important for models like Logistic Regression, SVM, and KNN, which are sensitive to differences in feature scale. Without standardization, variables such as distributed generation (measured in thousands of kW) would dominate smaller-scale features like wind speed or temperature, leading to biased learning.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Hyperparameter Tuning:\n", "\n", "To move beyond purely default model settings, we performed light but principled hyperparameter tuning on our main predictive models. For Logistic Regression, we evaluated two values of the regularization parameter C: the default C = 1, which applies moderate Ridge (L2) regularization to prevent overfitting, and a weaker regularization setting C = 10, which allows the model to fit the data more closely. For the Random Forest, we tuned the number of trees (100 and 200), the maximum tree depth (None, 10, and 20), and the minimum number of samples per leaf (1 and 5) using 3-fold cross-validation on the training set, selecting the configuration that maximized recall for congested feeders. For K-Nearest Neighbors (KNN), we tuned the number of neighbors (5, 10, 15, 20, and 30), again using recall as the selection metric. Smaller values of k capture highly local behavior but are more sensitive to noise, while larger values smooth predictions across broader operating conditions. By selecting k through cross-validation, we ensured that the final KNN model reflects a principled balance between bias and variances. The Linear SVM was evaluated using standard regularization settings to serve as an additional linear baseline for comparison against the nonlinear models." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_linear_loss.py:200: RuntimeWarning: divide by zero encountered in matmul\n", " raw_prediction = X @ weights + intercept\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_linear_loss.py:200: RuntimeWarning: overflow encountered in matmul\n", " raw_prediction = X @ weights + intercept\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_linear_loss.py:200: RuntimeWarning: invalid value encountered in matmul\n", " raw_prediction = X @ weights + intercept\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_linear_loss.py:330: RuntimeWarning: divide by zero encountered in matmul\n", " grad[:n_features] = X.T @ grad_pointwise + l2_reg_strength * weights\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_linear_loss.py:330: RuntimeWarning: overflow encountered in matmul\n", " grad[:n_features] = X.T @ grad_pointwise + l2_reg_strength * weights\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_linear_loss.py:330: RuntimeWarning: invalid value encountered in matmul\n", " grad[:n_features] = X.T @ grad_pointwise + l2_reg_strength * weights\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_linear_loss.py:200: RuntimeWarning: divide by zero encountered in matmul\n", " raw_prediction = X @ weights + intercept\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_linear_loss.py:200: RuntimeWarning: overflow encountered in matmul\n", " raw_prediction = X @ weights + intercept\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_linear_loss.py:200: RuntimeWarning: invalid value encountered in matmul\n", " raw_prediction = X @ weights + intercept\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_linear_loss.py:330: RuntimeWarning: divide by zero encountered in matmul\n", " grad[:n_features] = X.T @ grad_pointwise + l2_reg_strength * weights\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_linear_loss.py:330: RuntimeWarning: overflow encountered in matmul\n", " grad[:n_features] = X.T @ grad_pointwise + l2_reg_strength * weights\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_linear_loss.py:330: RuntimeWarning: invalid value encountered in matmul\n", " grad[:n_features] = X.T @ grad_pointwise + l2_reg_strength * weights\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Best Random Forest params: {'max_depth': None, 'min_samples_leaf': 1, 'n_estimators': 200}\n", "Best KNN params: {'n_neighbors': 5}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: divide by zero encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul\n", " ret = a @ b\n", "/Users/phealy92/Library/Python/3.9/lib/python/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul\n", " ret = a @ b\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " model accuracy precision recall roc_auc\n", "0 LogReg_default 0.800477 0.639439 0.423496 0.840412\n", "1 LogReg_C10 0.800458 0.639365 0.423496 0.840411\n", "2 LinearSVM 0.790844 0.636613 0.338628 0.840974\n", "3 RandomForest_Tuned 0.979531 0.965249 0.950583 0.997409\n", "4 KNN_Tuned 0.953992 0.909267 0.902010 0.986140\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from sklearn.model_selection import train_test_split, GridSearchCV\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.impute import SimpleImputer\n", "\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.svm import LinearSVC\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "from sklearn.metrics import (\n", " accuracy_score,\n", " precision_score,\n", " recall_score,\n", " roc_auc_score,\n", " classification_report,\n", " confusion_matrix,\n", " ConfusionMatrixDisplay,\n", " RocCurveDisplay,\n", ")\n", "\n", "df = pd.read_csv(\"/Users/phealy92/CascadeProjects/Predicted-Feeder-Congestion/outputs/feeder_features_cleaned.csv\")\n", "\n", "df[\"congested\"] = (df[\"mean_ica_sg\"] < 1000).astype(int)\n", "\n", "forbidden_cols = [\n", " \"nominal voltage (kv)\", \"ZIP_CODE\", \"loadorgen\", \"2023\",\n", " \"%change\", \"feederid\", \"redacted data\", \"voltage\", \"mean_ica_sg\"\n", "]\n", "\n", "cols_to_drop = [c for c in forbidden_cols + [\"congested\"] if c in df.columns]\n", "X = df.drop(columns=cols_to_drop)\n", "y = df[\"congested\"]\n", "\n", "X_encoded = pd.get_dummies(X, drop_first=True)\n", "\n", "imputer = SimpleImputer(strategy=\"median\")\n", "X_imputed = imputer.fit_transform(X_encoded)\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X_imputed, y, test_size=0.2, random_state=42, stratify=y\n", ")\n", "\n", "scaler = StandardScaler()\n", "X_train = scaler.fit_transform(X_train)\n", "X_test = scaler.transform(X_test)\n", "\n", "models = {}\n", "\n", "logreg = LogisticRegression(max_iter=1000)\n", "logreg.fit(X_train, y_train)\n", "models[\"LogReg_default\"] = logreg\n", "\n", "logreg_loose = LogisticRegression(C=10, max_iter=1000)\n", "logreg_loose.fit(X_train, y_train)\n", "models[\"LogReg_C10\"] = logreg_loose\n", "\n", "svm = LinearSVC()\n", "svm.fit(X_train, y_train)\n", "models[\"LinearSVM\"] = svm\n", "\n", "rf_param_grid = {\n", " \"n_estimators\": [100, 200],\n", " \"max_depth\": [None, 10, 20],\n", " \"min_samples_leaf\": [1, 5],\n", "}\n", "\n", "rf_base = RandomForestClassifier(random_state=42)\n", "rf_grid = GridSearchCV(\n", " rf_base, rf_param_grid, cv=3, scoring=\"recall\", n_jobs=-1\n", ")\n", "rf_grid.fit(X_train, y_train)\n", "best_rf = rf_grid.best_estimator_\n", "models[\"RandomForest_Tuned\"] = best_rf\n", "\n", "print(\"Best Random Forest params:\", rf_grid.best_params_)\n", "\n", "knn_param_grid = {\"n_neighbors\": [5, 10, 15, 20, 30]}\n", "knn_base = KNeighborsClassifier()\n", "knn_grid = GridSearchCV(\n", " knn_base, knn_param_grid, cv=3, scoring=\"recall\", n_jobs=-1\n", ")\n", "knn_grid.fit(X_train, y_train)\n", "best_knn = knn_grid.best_estimator_\n", "models[\"KNN_Tuned\"] = best_knn\n", "\n", "print(\"Best KNN params:\", knn_grid.best_params_)\n", "\n", "results = []\n", "\n", "for name, clf in models.items():\n", " y_pred = clf.predict(X_test)\n", " if hasattr(clf, \"predict_proba\"):\n", " y_scores = clf.predict_proba(X_test)[:, 1]\n", " elif hasattr(clf, \"decision_function\"):\n", " y_scores = clf.decision_function(X_test)\n", " else:\n", " y_scores = None\n", "\n", " acc = accuracy_score(y_test, y_pred)\n", " prec = precision_score(y_test, y_pred)\n", " rec = recall_score(y_test, y_pred)\n", " auc = roc_auc_score(y_test, y_scores) if y_scores is not None else np.nan\n", "\n", " results.append(\n", " {\"model\": name, \"accuracy\": acc, \"precision\": prec,\n", " \"recall\": rec, \"roc_auc\": auc}\n", " )\n", "\n", "results_df = pd.DataFrame(results)\n", "print(results_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Results Interpretation:\n", "\n", "After tuning, the best Random Forest model ended up using 200 trees, with no limit on how deep the trees can grow, and a minimum leaf size of 1. In simple terms, this means the model works best when it is allowed to be very detailed and flexible, rather than forcing it to stay simple. This tells us that feeder congestion depends on fine-grained, local conditions instead of broad averages. Small differences in weather, load, or customer mix can meaningfully change congestion risk, and the Random Forest is capturing that.\n", "\n", "For KNN, the best choice was k = 5, meaning the model looks at only the five most similar past situations to make a prediction. This shows that congestion tends to happen under very specific operating conditions, and the most similar historical cases are the most informative. Using more neighbors would smooth things too much and hide these local effects.\n", "\n", "When we compare overall performance, the tuned Random Forest clearly performs the best: It gets almost 98% of all predictions correct, and correctly identifies about 95% of truly congested feeders.\n", "\n", "The tuned KNN model also performs well, with over 90% recall, showing that local similarity is a strong predictor of congestion. However, it is still less consistent than the Random Forest and more sensitive to noise.\n", "\n", "In contrast, the linear models (Logistic Regression and Linear SVM) perform much worse at detecting congestion, missing more than half of actual congestion events. The fact that Logistic Regression performs the same with both C = 1 and C = 10 confirms that the problem is not about tuning — it’s that congestion simply isn’t a linear process." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'y_pred_rf' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m cm \u001b[38;5;241m=\u001b[39m confusion_matrix(y_test, \u001b[43my_pred_rf\u001b[49m)\n\u001b[1;32m 2\u001b[0m disp \u001b[38;5;241m=\u001b[39m ConfusionMatrixDisplay(confusion_matrix\u001b[38;5;241m=\u001b[39mcm, display_labels\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m 4\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure()\n", "\u001b[0;31mNameError\u001b[0m: name 'y_pred_rf' is not defined" ] } ], "source": [ "cm = confusion_matrix(y_test, y_pred_rf)\n", "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[0, 1])\n", "\n", "plt.figure()\n", "disp.plot(values_format='d')\n", "plt.title(\"Confusion Matrix - Random Forest\")\n", "plt.show()\n", "\n", "importances = models['RandomForest_Tuned'].feature_importances_\n", "feature_names = X_encoded.columns\n", "\n", "idx = np.argsort(importances)[-20:]\n", "top_importances = importances[idx]\n", "top_features = feature_names[idx]\n", "\n", "plt.figure(figsize=(8, 6))\n", "plt.barh(top_features, top_importances)\n", "plt.title(\"Top 20 Feature Importances - Random Forest\")\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the Random Forest clearly performed the best among all models, we focused our deeper analysis on this model. To better understand both its predictive behavior and practical usefulness, we generated a confusion matrix to examine how well it distinguishes between congested and non-congested feeders in terms of true positives, false positives, true negatives, and false negatives. In addition, we analyzed the feature importance scores produced by the Random Forest to identify which variables contribute most to congestion prediction." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Prediction Model 3: Multi-Category Classification:\n", "\n", "For Prediction Problem 3, we treat feeder congestion as a three-tier classification task in which the continuous headroom variable (mean_ica_sg) is converted into low-, medium-, and high-risk categories based on practical engineering thresholds (>5 MW, 1–5 MW, and <1 MW, respectively). This allows us to translate raw feeder capacity values into a simplified, decision-friendly framework for understanding congestion risk. Our feature matrix includes all numeric variables in the cleaned dataset aside from the response, capturing both slow-moving feeder attributes, such as voltage level and customer mix, and more dynamic characteristics like time-of-day and weather. Before modeling, we apply simple median imputation to handle missing numeric values and standardize all features; scaling is required for logistic regression and helps maintain consistency across pipelines even though this is not necessary for decision tree and random forest models.\n", "\n", "To explore modeling performance across different levels of complexity, we train three classifiers: a multinomial logistic regression model as our linear baseline, a single decision tree as an interpretable non-linear model, and a random forest as a higher-capacity ensemble. Logistic regression provides a useful reference point because it is fast, stable, and yields coefficients that directly indicate how each feature influences the likelihood of belonging to each congestion tier. The decision tree relaxes linearity assumptions and offers threshold-based decision rules that resemble the way system engineers reason about feeders. The random forest combines many trees to reduce variance and capture more complex interactions between feeder characteristics and temporal variables, generally improving predictive performance at the cost of reduced interpretability. We do not consider KNN here, as KNN scales poorly with both sample size and dimensionality.\n", "\n", "We partition the data into training and testing subsets using an 80/20 stratified split to preserve the imbalance in congestion tiers. Unlike a full hyperparameter search, we use fixed but reasonable model configurations based on prior experience and light experimentation, which keeps the focus on comparing representative versions of each model rather than performing an exhaustive tuning process. Model evaluation is carried out entirely on the held-out test set using accuracy, macro-averaged F1, class-specific precision and recall, and confusion matrices. Macro F1 is especially informative for our problem because it weights all congestion tiers equally, preventing the majority class from dominating overall performance metrics. Finally, we examine model interpretability by inspecting coefficients for logistic regression and feature importances for both the decision tree and random forest. Throughout the workflow, we structure our code to remain concise, readable, and maintainable using functions that minimize duplicative effort. The code snippet below includes all 3 tests and their corresponding visualizations: \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Tier distribution:\n", "0 42459\n", "1 158640\n", "2 65159\n", "Name: count, dtype: int64\n", " Model Accuracy Precision Recall Macro F1\n", "0 Logistic Regression 0.696 0.681 0.608 0.634\n", "1 Decision Tree 0.732 0.747 0.624 0.662\n", "2 Random Forest 0.970 0.968 0.967 0.968\n" ] } ], "source": [ "# 1. Imports\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.pipeline import make_pipeline\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.impute import SimpleImputer\n", "\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.ensemble import RandomForestClassifier\n", "\n", "from sklearn.metrics import (\n", " confusion_matrix,\n", " ConfusionMatrixDisplay,\n", " accuracy_score,\n", " classification_report,\n", " f1_score,\n", ")\n", "\n", "# 2. Load data\n", "csv_path = \"outputs/feeder_features_cleaned_PhillipEdits_v2.csv\"\n", "df = pd.read_csv(csv_path)\n", "\n", "response_col = \"mean_ica_sg\"\n", "df = df.dropna(subset=[response_col])\n", "\n", "# 3. Define features and create 3-class target variable\n", "numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()\n", "feature_cols = [c for c in numeric_cols if c != response_col]\n", "\n", "X = df[feature_cols]\n", "headroom = df[response_col]\n", "\n", "# Define 3 risk tiers\n", "conditions = [\n", " headroom > 5000.0,\n", " (headroom >= 1000.0) & (headroom <= 5000.0),\n", " headroom < 1000.0,\n", "]\n", "choices = [0, 1, 2]\n", "y_tier = np.select(conditions, choices).astype(int) \n", "\n", "print(\"\\nTier distribution:\")\n", "print(pd.Series(y_tier).value_counts().sort_index())\n", "\n", "# 4. Train/test split\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X,\n", " y_tier,\n", " test_size=0.2,\n", " random_state=42,\n", " stratify=y_tier, #ensure train and test sets are balanced \n", ")\n", "\n", "# 5. Helper function for printing metrics\n", "def print_multiclass_metrics(y_true, y_pred, label=\"\"):\n", " acc = accuracy_score(y_true, y_pred)\n", " macro_f1 = f1_score(y_true, y_pred, average=\"macro\")\n", "\n", " print(f\"\\n{label}\")\n", " print(f\"Accuracy: {acc:.3f}\") #float 3 decimals\n", " print(f\"Macro F1: {macro_f1:.3f}\")\n", " print(\"Classification report:\")\n", " print(classification_report(y_true, y_pred))\n", "\n", "# 6. Model Training\n", "# (A) Logistic Regression \n", "log_model = make_pipeline(\n", " SimpleImputer(strategy=\"median\"), # Use median imputation to handle missing numeric values robustly.\n", " StandardScaler(), # Scale features because logistic regression performs best on standardized data.\n", " LogisticRegression(\n", " multi_class=\"multinomial\", # Use true multinomial (softmax) for multi-class prediction instead of one-vs-rest.\n", " solver=\"lbfgs\", # LBFGS is stable and efficient for multinomial logistic regression.\n", " max_iter=500, # Increase iterations to ensure the model converges on a large dataset.\n", " random_state=42 # Fix random seed for reproducible model behavior.\n", " ),\n", ")\n", "\n", "# (B) Decision Tree\n", "dt_model = make_pipeline(\n", " SimpleImputer(strategy=\"median\"), # Fill missing numeric values with the median to avoid losing data.\n", " StandardScaler(), # Included for pipeline consistency; trees are scale-invariant.\n", " DecisionTreeClassifier(\n", " max_depth=5, # Limit tree depth to prevent overfitting and improve generalization.\n", " min_samples_leaf=50, # Require at least 50 samples per leaf to smooth out noisy splits.\n", " random_state=42, # Fix random seed for reproducible tree structure.\n", " ),\n", ")\n", "\n", "# (C) Random Forest\n", "rf_model = make_pipeline(\n", " SimpleImputer(strategy=\"median\"),\n", " StandardScaler(),\n", " RandomForestClassifier(\n", " n_estimators=200,\n", " random_state=42,\n", " n_jobs=-1,\n", " ),\n", ")\n", "\n", "results = []\n", "\n", "results.append({\n", " \"Model\": \"Logistic Regression\",\n", " \"Accuracy\": accuracy_score(y_test, y_pred_log),\n", " \"Precision\": precision_score(y_test, y_pred_log, average=\"macro\"),\n", " \"Recall\": recall_score(y_test, y_pred_log, average=\"macro\"),\n", " \"Macro F1\": f1_score(y_test, y_pred_log, average=\"macro\")\n", "})\n", "\n", "results.append({\n", " \"Model\": \"Decision Tree\",\n", " \"Accuracy\": accuracy_score(y_test, y_pred_dt),\n", " \"Precision\": precision_score(y_test, y_pred_dt, average=\"macro\"),\n", " \"Recall\": recall_score(y_test, y_pred_dt, average=\"macro\"),\n", " \"Macro F1\": f1_score(y_test, y_pred_dt, average=\"macro\")\n", "})\n", "\n", "results.append({\n", " \"Model\": \"Random Forest\",\n", " \"Accuracy\": accuracy_score(y_test, y_pred_rf),\n", " \"Precision\": precision_score(y_test, y_pred_rf, average=\"macro\"),\n", " \"Recall\": recall_score(y_test, y_pred_rf, average=\"macro\"),\n", " \"Macro F1\": f1_score(y_test, y_pred_rf, average=\"macro\")\n", "})\n", "\n", "results_df = pd.DataFrame(results)\n", "print(results_df.round(3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This table compares overall performance across the three models.\n", "The random forest dramatically outperforms both logistic regression and the decision tree, achieving very high accuracy and macro-F1, while the other two models show moderate performance and struggle with class imbalance.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 7. Visualizations - here we plot our results as confusion matrices\n", "tier_labels = [\"Low risk (0)\", \"Med risk (1)\", \"High risk (2)\"]\n", "\n", "# Logistic Regression Confusion Matrix \n", "cm_log = confusion_matrix(y_test, y_pred_log, labels=[0, 1, 2])\n", "# Reverse the columns for x-axis reversal\n", "cm_log_reversed = cm_log[:, ::-1]\n", "\n", "# Create custom display with reversed labels for x-axis\n", "disp_log = ConfusionMatrixDisplay(confusion_matrix=cm_log_reversed, \n", " display_labels=tier_labels[::-1])\n", "\n", "plt.figure(figsize=(6, 5))\n", "disp_log.plot(cmap=\"Purples\", values_format=\"d\")\n", "plt.title(\"Logistic Regression (Softmax) — Test Set Tier Categorization\")\n", "\n", "# Manually set the y-axis labels in the correct order\n", "ax = plt.gca()\n", "ax.set_yticklabels(tier_labels)\n", "ax.set_xticklabels(tier_labels[::-1]) # Keep x-axis reversed\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "# Decision Tree Confusion Matrix \n", "cm_dt = confusion_matrix(y_test, y_pred_dt, labels=[0, 1, 2])\n", "cm_dt_reversed = cm_dt[:, ::-1]\n", "\n", "disp_dt = ConfusionMatrixDisplay(confusion_matrix=cm_dt_reversed, \n", " display_labels=tier_labels[::-1])\n", "\n", "plt.figure(figsize=(6, 5))\n", "disp_dt.plot(cmap=\"Reds\", values_format=\"d\")\n", "plt.title(\"Decision Tree — Test Set Tier Categorization\")\n", "\n", "ax = plt.gca()\n", "ax.set_yticklabels(tier_labels)\n", "ax.set_xticklabels(tier_labels[::-1])\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "# Random Forest Confusion Matrix \n", "cm_rf = confusion_matrix(y_test, y_pred_rf, labels=[0, 1, 2])\n", "cm_rf_reversed = cm_rf[:, ::-1]\n", "\n", "disp_rf = ConfusionMatrixDisplay(confusion_matrix=cm_rf_reversed, \n", " display_labels=tier_labels[::-1])\n", "\n", "plt.figure(figsize=(6, 5))\n", "disp_rf.plot(cmap=\"Blues\", values_format=\"d\")\n", "plt.title(\"Random Forest — Test Set Tier Categorization\")\n", "\n", "ax = plt.gca()\n", "ax.set_yticklabels(tier_labels)\n", "ax.set_xticklabels(tier_labels[::-1])\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Logistic regression provides a solid linear baseline but struggles with Tier 0 and Tier 2 due to its limited capacity to model non-linear patterns. It performs best on the majority Tier 1 class, with noticeably lower recall for high-risk feeders.\n", "\n", "The decision tree improves on logistic regression by capturing non-linear thresholds, especially boosting Tier 1 performance, but still misses many Tier 0 and Tier 2 cases. Its overall performance is moderate, reflecting the limitations of using a shallow, interpretable model on a complex classification task.\n", "\n", "The random forest achieves excellent performance across all tiers, with high precision, recall, and F1, indicating strong ability to capture complex interactions in the data. Its consistently high scores suggest far better predictive power, though it may risk overfitting compared to simpler models.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Resource Allocation and Conclusions (20 points)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The purpose of this project was to see whether feeder-level headroom and congestion risk could be predicted using publicly available data on load, weather, customer mix, and DER adoption. The three prediction problems taken together show that feeder behavior is patterned enough that models can provide useful guidance, although with meaningful uncertainty. The Random Forest generally performed better than linear models in every task, suggesting that congestion depends on interactions across several variables rather than simple trends. These results help link the forecasts to practical decisions about where to invest limited utility resources, which feeders should be monitored more closely, and where new DER projects are most likely to be feasible.\n", "\n", "For the first prediction problem, the model estimating month-hour feeder load gives planners a rough sense of how much headroom remains before reaching a thermal or ICA limit. The Random Forest model’s errors were approximately 240 kW, which is small compared to feeders in the 10 to 15 MW range. This accuracy allows planners to use the predictions as one input when reviewing DER interconnection requests or assessing whether a feeder might need additional support. For instance, if a feeder has a 12 MW limit and the model predicts a load near 10.5 MW, that suggests that a small solar project or EV charger could likely be approved without major concern. If the forecast is closer to 11.7 or 11.8 MW, the feeder may need a closer look or a more conservative upgrade timeline. These insights can help prevent unnecessary upgrades, reduce delays, and direct funds toward feeders that appear to be trending toward constraint.\n", "\n", "For the second prediction problem, the tuned Random Forest model delivered near-perfect performance, with an accuracy of 97.95%, precision of 96.52%, recall of 95.06%, and a ROC–AUC of 0.997. From a resource allocation perspective, the most important metric is recall, because false negatives represent missed congested feeders that can lead to interconnection delays, emergency upgrades, and reliability failures. With over 95% of truly congested cases correctly identified, the model allows utilities to confidently prioritize field inspections, targeted upgrades, and operational mitigation on feeders flagged as congested, while safely deferring action on feeders predicted to be uncongested. Quantitatively, this level of performance sharply reduces the risk of under-investment in constrained areas while avoiding unnecessary upgrades on low-risk feeders. In practice, this enables planners to shift from conservative system-wide spending toward highly targeted, feeder-specific investment, improving both cost efficiency and system reliability under growing electrification pressure.\n", "\n", "The third prediction problem assigns observations into low, medium, or high congestion tiers, which helps longer-term planning and triage. The Random Forest captured the majority of high-risk cases, while simpler models struggled. If a feeder repeatedly falls into the highest-risk tier, that suggests it should be considered seriously for future capital planning. Feeders that sit mainly in the middle tier might not require immediate upgrades but could become riskier as electrification and DER adoption increase. This tiered approach helps utilities organize multi-year budgets and identify which locations may need more detailed engineering study.\n", "\n", "Despite the strong performance of the Random Forest models, there are several sources of uncertainty that limit how confidently the results should be interpreted. The data were aggregated to the month-hour level, which smooths out sharp load spikes and extreme weather events. This means that true peak stress may be higher than the model predicts. EV adoption data were only available annually at the ZIP code level, and this likely understates short-term charging impacts. Weather data were matched to feeders through ZIP codes and regional weather stations, which may not fully capture local microclimates. These factors tend to bias estimates downward, suggesting that the model may understate congestion during unusual or extreme conditions.\n", "\n", "The models are also purely data-driven. They do not enforce power system physics such as thermal limits, voltage performance, protection behavior, or feeder topology. If future operating conditions differ significantly from the historical data, such as rapid electrification or very high DER penetration, the models may not generalize well. The tiered classification problem is affected by class imbalance, making it easier for some models to default to the most common category. Even the Random Forest, though more accurate, may still overfit feeder-specific behaviors that would not transfer to other systems or future years.\n", "\n", "Taken together, these limitations suggest that the results should be treated as helpful planning guidance rather than exact operational predictions. The uncertainty generally leans toward underestimating congestion risk, meaning that feeders identified as high-risk should be taken seriously, while feeders labeled low-risk should still be monitored with some caution. Even with these caveats, the models provide a practical and lightweight way to complement engineering studies and help program administrators deploy storage and load shifting programs, while helping utilities manage DER growth by modeling various scenarios with forecasted feature data. By bypassing the high operational costs associated with ICA map production, stakeholders can more quickly and cost-effectively identify where to focus their limited resources.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.6" } }, "nbformat": 4, "nbformat_minor": 4 }