175 lines
7.9 KiB
Python
175 lines
7.9 KiB
Python
import pandas as pd
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import numpy as np
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import math, sys, random
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import yaml
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from sklearn.impute import KNNImputer
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from sklearn.preprocessing import StandardScaler
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import matplotlib.pyplot as plt
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import seaborn as sns
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sys.path.append('/rapids/')
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from src.features import empatica_data_yield as edy
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pd.set_option('display.max_columns', 20)
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def straw_cleaning(sensor_data_files, provider):
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# TODO (maybe): reorganize the script based on the overall
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features = pd.read_csv(sensor_data_files["sensor_data"][0])
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esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
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with open('config.yaml', 'r') as stream:
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config = yaml.load(stream, Loader=yaml.FullLoader)
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excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime']
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# (1) FILTER_OUT THE ROWS THAT DO NOT HAVE THE TARGET COLUMN AVAILABLE
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if config['PARAMS_FOR_ANALYSIS']['TARGET']['COMPUTE']:
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target = config['PARAMS_FOR_ANALYSIS']['TARGET']['LABEL'] # get target label from config
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features = features[features['phone_esm_straw_' + target].notna()].reset_index(drop=True)
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# (2.1) QUALITY CHECK (DATA YIELD COLUMN) deletes the rows where E4 or phone data is low quality
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phone_data_yield_unit = provider["PHONE_DATA_YIELD_FEATURE"].split("_")[3].lower()
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phone_data_yield_column = "phone_data_yield_rapids_ratiovalidyielded" + phone_data_yield_unit
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features = edy.calculate_empatica_data_yield(features)
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if not phone_data_yield_column in features.columns and not "empatica_data_yield" in features.columns:
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raise KeyError(f"RAPIDS provider needs to clean the selected event features based on {phone_data_yield_column} and empatica_data_yield columns. For phone data yield, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded{data_yield_unit}' in [FEATURES].")
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# Drop rows where phone data yield is less then given threshold
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if provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"]:
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features = features[features[phone_data_yield_column] >= provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"]].reset_index(drop=True)
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# Drop rows where empatica data yield is less then given threshold
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if provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]:
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features = features[features["empatica_data_yield"] >= provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]].reset_index(drop=True)
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if features.empty:
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return features
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# (2.2) DO THE ROWS CONSIST OF ENOUGH NON-NAN VALUES?
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min_count = math.ceil((1 - provider["ROWS_NAN_THRESHOLD"]) * features.shape[1]) # minimal not nan values in row
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features.dropna(axis=0, thresh=min_count, inplace=True) # Thresh => at least this many not-nans
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# (3) REMOVE COLS IF THEIR NAN THRESHOLD IS PASSED (should be <= if even all NaN columns must be preserved - this solution now drops columns with all NaN rows)
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esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
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features = features.loc[:, features.isna().sum() < provider["COLS_NAN_THRESHOLD"] * features.shape[0]]
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# Preserve esm cols if deleted (has to come after drop cols operations)
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for esm in esm_cols:
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if esm not in features:
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features[esm] = esm_cols[esm]
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# (4) CONTEXTUAL IMPUTATION
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# Impute selected phone features with a high number
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impute_w_hn = [col for col in features.columns if \
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"timeoffirstuse" in col or
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"timeoflastuse" in col or
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"timefirstcall" in col or
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"timelastcall" in col or
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"firstuseafter" in col or
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"timefirstmessages" in col or
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"timelastmessages" in col]
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features[impute_w_hn] = features[impute_w_hn].fillna(1500)
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# Impute special case (mostcommonactivity) and (homelabel)
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impute_w_sn = [col for col in features.columns if "mostcommonactivity" in col]
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features[impute_w_sn] = features[impute_w_sn].fillna(4) # Special case of imputation - nominal/ordinal value
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impute_w_sn2 = [col for col in features.columns if "homelabel" in col]
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features[impute_w_sn2] = features[impute_w_sn2].fillna(1) # Special case of imputation - nominal/ordinal value
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impute_w_sn3 = [col for col features.columns if "loglocationvariance" in col]
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features[impute_w_sn2] = features[impute_w_sn2].fillna(-1000000) # Special case of imputation - nominal/ordinal value
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# Impute selected phone features with 0
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impute_zero = [col for col in features if \
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col.startswith('phone_applications_foreground_rapids_') or
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col.startswith('phone_battery_rapids_') or
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col.startswith('phone_bluetooth_rapids_') or
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col.startswith('phone_light_rapids_') or
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col.startswith('phone_calls_rapids_') or
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col.startswith('phone_messages_rapids_') or
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col.startswith('phone_screen_rapids_') or
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col.startswith('phone_wifi_visible')]
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features[impute_zero] = features[impute_zero].fillna(0)
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## (5) STANDARDIZATION
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if provider["STANDARDIZATION"]:
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features.loc[:, ~features.columns.isin(excluded_columns)] = StandardScaler().fit_transform(features.loc[:, ~features.columns.isin(excluded_columns)])
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# (6) IMPUTATION: IMPUTE DATA WITH KNN METHOD
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impute_cols = [col for col in features.columns if col not in excluded_columns]
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features.reset_index(drop=True, inplace=True)
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features[impute_cols] = impute(features[impute_cols], method="knn")
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# (7) REMOVE COLS WHERE VARIANCE IS 0
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esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')]
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if provider["COLS_VAR_THRESHOLD"]:
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features.drop(features.std()[features.std() == 0].index.values, axis=1, inplace=True)
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fe5 = features.copy()
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# (8) DROP HIGHLY CORRELATED FEATURES
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drop_corr_features = provider["DROP_HIGHLY_CORRELATED_FEATURES"]
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if drop_corr_features["COMPUTE"] and features.shape[0]: # If small amount of segments (rows) is present, do not execute correlation check
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numerical_cols = features.select_dtypes(include=np.number).columns.tolist()
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# Remove columns where NaN count threshold is passed
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valid_features = features[numerical_cols].loc[:, features[numerical_cols].isna().sum() < drop_corr_features['MIN_OVERLAP_FOR_CORR_THRESHOLD'] * features[numerical_cols].shape[0]]
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corr_matrix = valid_features.corr().abs()
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upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))
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to_drop = [column for column in upper.columns if any(upper[column] > drop_corr_features["CORR_THRESHOLD"])]
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features.drop(to_drop, axis=1, inplace=True)
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# Preserve esm cols if deleted (has to come after drop cols operations)
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for esm in esm_cols:
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if esm not in features:
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features[esm] = esm_cols[esm]
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fe6 = features.copy()
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# (9) VERIFY IF THERE ARE ANY NANS LEFT IN THE DATAFRAME
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if features.isna().any().any():
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raise ValueError("There are still some NaNs present in the dataframe. Please check for implementation errors.")
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return features
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def impute(df, method='zero'):
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def k_nearest(df):
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pd.set_option('display.max_columns', None)
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imputer = KNNImputer(n_neighbors=3)
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return pd.DataFrame(imputer.fit_transform(df), columns=df.columns)
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return {
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'zero': df.fillna(0),
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'high_number': df.fillna(1500),
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'mean': df.fillna(df.mean()),
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'median': df.fillna(df.median()),
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'knn': k_nearest(df)
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}[method]
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def graph_bf_af(features, phase_name, plt_flag=False):
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if plt_flag:
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sns.set(rc={"figure.figsize":(16, 8)})
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sns.heatmap(features.isna(), cbar=False) #features.select_dtypes(include=np.number)
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plt.savefig(f'features_overall_nans_{phase_name}.png', bbox_inches='tight')
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print(f"\n-------------{phase_name}-------------")
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print("Rows number:", features.shape[0])
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print("Columns number:", len(features.columns))
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print("---------------------------------------------\n")
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