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@ -6,7 +6,7 @@
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# extension: .py
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# format_name: percent
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# format_version: '1.3'
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# jupytext_version: 1.12.0
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# jupytext_version: 1.13.0
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# kernelspec:
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# display_name: straw2analysis
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# language: python
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@ -21,11 +21,15 @@ import os
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import sys
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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import seaborn as sns
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import yaml
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from pyprojroot import here
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from sklearn import linear_model
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from sklearn.model_selection import LeaveOneGroupOut, cross_val_score
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from sklearn.metrics import mean_squared_error, r2_score
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from sklearn.impute import SimpleImputer
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nb_dir = os.path.split(os.getcwd())[0]
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if nb_dir not in sys.path:
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@ -257,4 +261,195 @@ model_validation.cross_validate()
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# %%
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model_validation.groups
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# %% [markdown]
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# # Use RAPIDS
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# %%
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with open(here("rapids/config.yaml"), "r") as file:
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rapids_config = yaml.safe_load(file)
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# %%
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for key in rapids_config.keys():
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if isinstance(rapids_config[key], dict): # Remove top-level configs
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if ("PROVIDERS" in rapids_config[key]): # Retain features (that have providers)
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if rapids_config[key]["PROVIDERS"]: # Remove non-implemented features
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for provider in rapids_config[key]["PROVIDERS"]:
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if rapids_config[key]["PROVIDERS"][provider]["COMPUTE"]: # Check that the features were actually calculated
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if "FEATURES" in rapids_config[key]["PROVIDERS"][provider]:
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print(key)
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print(provider)
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print(rapids_config[key]["PROVIDERS"][provider]["FEATURES"])
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# %%
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features_rapids = pd.read_csv(here("rapids/data/processed/features/all_participants/all_sensor_features.csv"), parse_dates=["local_segment_start_datetime", "local_segment_end_datetime"])
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# %%
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features_rapids.columns
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# %%
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features_rapids = features_rapids.assign(date_lj=lambda x: x.local_segment_start_datetime.dt.date)
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# %%
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features_rapids["participant_id"] = features_rapids["pid"].str.extract("(\d+)")
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features_rapids["participant_id"] = pd.to_numeric(features_rapids["participant_id"])
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features_rapids.set_index(["participant_id", "date_lj"], inplace=True)
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# %%
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with open("../machine_learning/config/minimal_labels.yaml", "r") as file:
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labels_params = yaml.safe_load(file)
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# %%
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labels = machine_learning.labels.Labels(**labels_params)
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labels.set_participants_label("all")
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# %%
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labels.aggregate_labels(cached=True)
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labels_read = labels.get_aggregated_labels()
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labels_read = labels_read.reset_index()
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labels_read["date_lj"] = labels_read["date_lj"].dt.date
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labels_read.set_index(["participant_id", "date_lj"], inplace=True)
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# date_lj column is parsed as a date and represented as Timestamp, when read from csv.
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# When calculated, it is represented as date.
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# %%
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features_rapids.shape
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# %%
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labels_read.shape
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# %%
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features_labels = features_rapids.join(labels_read, how="inner").reset_index()
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# %%
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features_labels.shape
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# %%
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features_labels.columns
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# %%
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imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
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# %%
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feature_columns = features_labels.columns[6:-3]
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label_column = "NA"
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group_column = "pid"
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# %%
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lin_reg_rapids = linear_model.LinearRegression()
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logo = LeaveOneGroupOut()
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logo.get_n_splits(
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features_labels[feature_columns],
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features_labels[label_column],
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groups=features_labels[group_column],
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)
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# %%
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cross_val_score(
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lin_reg_rapids,
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X=imputer.fit_transform(features_labels[feature_columns]),
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y=features_labels[label_column],
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groups=features_labels[group_column],
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cv=logo,
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n_jobs=-1,
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scoring="r2",
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)
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# %%
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sns.set(rc={"figure.figsize":(16, 8)})
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sns.heatmap(features_labels[feature_columns].isna(), cbar=False)
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# %% [markdown] tags=[]
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# ```yaml
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# ALL_CLEANING_INDIVIDUAL:
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# PROVIDERS:
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# RAPIDS:
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# COMPUTE: True
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# IMPUTE_SELECTED_EVENT_FEATURES: # Fill NAs with 0 only for event-based features, see table below
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# COMPUTE: True
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# MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33 # Any feature value in a time segment instance with phone data yield > [MIN_DATA_YIELDED_MINUTES_TO_IMPUTE] will be replaced with a zero.
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# COLS_NAN_THRESHOLD: 0.3 # Discard columns with missing value ratios higher than [COLS_NAN_THRESHOLD]. Set to 1 to disable
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# COLS_VAR_THRESHOLD: True # Set to True to discard columns with zero variance
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# ROWS_NAN_THRESHOLD: 1 # Discard rows with missing value ratios higher than [ROWS_NAN_THRESHOLD]. Set to 1 to disable
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# DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
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# DATA_YIELD_RATIO_THRESHOLD: 0.3 # Discard rows with ratiovalidyieldedhours or ratiovalidyieldedminutes feature less than [DATA_YIELD_RATIO_THRESHOLD]. The feature name is determined by [DATA_YIELD_FEATURE] parameter. Set to 0 to disable
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# DROP_HIGHLY_CORRELATED_FEATURES:
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# COMPUTE: False
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# MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
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# CORR_THRESHOLD: 0.95
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# SRC_SCRIPT: src/features/all_cleaning_individual/rapids/main.R
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# ```
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# %%
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features_rapids_cleaned = pd.read_csv(here("rapids/data/processed/features/all_participants/all_sensor_features_cleaned_rapids.csv"), parse_dates=["local_segment_start_datetime", "local_segment_end_datetime"])
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features_rapids_cleaned = features_rapids_cleaned.assign(date_lj=lambda x: x.local_segment_start_datetime.dt.date)
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features_rapids_cleaned["participant_id"] = features_rapids_cleaned["pid"].str.extract("(\d+)")
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features_rapids_cleaned["participant_id"] = pd.to_numeric(features_rapids_cleaned["participant_id"])
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features_rapids_cleaned.set_index(["participant_id", "date_lj"], inplace=True)
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# %%
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features_cleaned_labels = features_rapids_cleaned.join(labels_read, how="inner").reset_index()
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feature_clean_columns = features_cleaned_labels.columns[6:-3]
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# %%
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print(feature_columns.shape)
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print(feature_clean_columns.shape)
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# %%
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sns.set(rc={"figure.figsize":(16, 8)})
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sns.heatmap(features_cleaned_labels[feature_clean_columns].isna(), cbar=False)
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# %%
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lin_reg_rapids_clean = linear_model.LinearRegression()
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logo = LeaveOneGroupOut()
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logo.get_n_splits(
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features_cleaned_labels[feature_clean_columns],
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features_cleaned_labels[label_column],
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groups=features_cleaned_labels[group_column],
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)
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# %%
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features_clean_imputed = imputer.fit_transform(features_cleaned_labels[feature_clean_columns])
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# %%
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cross_val_score(
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lin_reg_rapids_clean,
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X=features_clean_imputed,
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y=features_cleaned_labels[label_column],
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groups=features_cleaned_labels[group_column],
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cv=logo,
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n_jobs=-1,
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scoring="r2",
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)
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# %%
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lin_reg_full = linear_model.LinearRegression()
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lin_reg_full.fit(features_clean_imputed,features_cleaned_labels[label_column])
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# %%
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NA_pred = lin_reg_full.predict(features_clean_imputed)
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# %%
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# The coefficients
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print("Coefficients: \n", lin_reg_full.coef_)
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# The mean squared error
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print("Mean squared error: %.2f" % mean_squared_error(features_cleaned_labels[label_column], NA_pred))
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# The coefficient of determination: 1 is perfect prediction
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print("Coefficient of determination: %.2f" % r2_score(features_cleaned_labels[label_column], NA_pred))
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# %%
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feature_clean_columns[np.argmax(lin_reg_full.coef_)]
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# %% [markdown]
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# Ratio between stationary time and total location sensed time. A lat/long coordinate pair is labeled as stationary if its speed (distance/time) to the next coordinate pair is less than 1km/hr. A higher value represents a more stationary routine.
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# %%
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plt.scatter(features_clean_imputed[:,np.argmax(lin_reg_full.coef_)], features_cleaned_labels[label_column], color="black")
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plt.scatter(features_clean_imputed[:,np.argmax(lin_reg_full.coef_)], NA_pred, color="red", linewidth=3)
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plt.xticks()
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plt.yticks()
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fig = plt.gcf()
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fig.set_size_inches(18.5, 10.5)
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plt.show()
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junos