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Changes in phone features NaN values script.

notes
Primoz 2022-09-16 09:03:30 +00:00
parent 385e21409d
commit 7d85f75d21
2 changed files with 285 additions and 84 deletions
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84
tests/scripts/doryab_values.py
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import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
path = "/rapids/data/processed/features/all_participants/all_sensor_features.csv"
df = pd.read_csv(path)
# Bluetooth
doryab_cols_bt = [col for col in df.columns if "bluetooth_doryab" in col]
df_bt = df[doryab_cols_bt]
print(len(doryab_cols_bt))
print(df_bt)
df_bt = df_bt.dropna(axis=0, how="all")
sns.heatmap(df_bt.isna(), xticklabels=1)
plt.savefig(f'bluetooth_doryab_values', bbox_inches='tight')
df_q = pd.DataFrame()
for col in df_bt:
df_q[col] = pd.to_numeric(pd.cut(df_bt[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
sns.heatmap(df_q, cbar=False, xticklabels=1)
plt.savefig(f'cut_bluetooth_doryab_values', bbox_inches='tight')
plt.close()
# Location
doryab_cols_loc = [col for col in df.columns if "locations_doryab" in col]
df_loc = df[doryab_cols_loc]
print(len(doryab_cols_loc))
print(df_loc)
df_loc = df_loc.dropna(axis=0, how="all").reset_index(drop=True)
print(df_loc)
sns.heatmap(df_loc.isna())
plt.savefig(f'locations_doryab_values', bbox_inches='tight')
df_q = pd.DataFrame()
for col in df_loc:
df_q[col] = pd.to_numeric(pd.cut(df_loc[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
sns.heatmap(df_q, cbar=False, xticklabels=1)
plt.savefig(f'cut_location_doryab_values', bbox_inches='tight')
plt.close()
plt.plot(df_loc['phone_locations_doryab_loglocationvariance'])
plt.savefig(f'phone_locations_doryab_loglocationvariance', bbox_inches='tight')
plt.close()
# Phone calls & messages
calls_sms_cols = [col for col in df.columns if "phone_calls" in col or "phone_messages" in col]
df_cs= df[calls_sms_cols]
print(len(calls_sms_cols))
print(df_cs)
df_cs = df_cs.dropna(axis=0, how="all")
sns.heatmap(df_cs.isna(), xticklabels=1)
plt.savefig(f'calls_sms_values', bbox_inches='tight')
df_q = pd.DataFrame()
for col in df_cs:
df_q[col] = pd.to_numeric(pd.cut(df_cs[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
sns.heatmap(df_q, cbar=False, xticklabels=1)
plt.savefig(f'cut_calls_sms_values', bbox_inches='tight')
plt.close()
# All features
print(len(df))
print(df)
df = df.dropna(axis=0, how="all")
df = df.dropna(axis=1, how="all")
sns.heatmap(df.isna())
plt.savefig(f'all_features', bbox_inches='tight')
print(df.columns[df.isna().all()].tolist())
print("All NaNs:", df.isna().sum().sum())
print("Df shape NaNs:", df.shape)

285
tests/scripts/phone_feats.py 100644
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import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
path = "/rapids/data/processed/features/all_participants/all_sensor_features.csv"
df = pd.read_csv(path)
# activity_recognition
cols = [col for col in df.columns if "activity_recognition" in col]
df_x = df[cols]
print(len(cols))
print(df_x)
df_x = df_x.dropna(axis=0, how="all")
sns.heatmap(df_x.isna(), xticklabels=1)
plt.savefig(f'activity_recognition_values', bbox_inches='tight')
df_q = pd.DataFrame()
for col in df_x:
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
sns.heatmap(df_q, cbar=False, xticklabels=1)
plt.savefig(f'cut_activity_recognition_values', bbox_inches='tight')
plt.close()
# applications_foreground
cols = [col for col in df.columns if "applications_foreground" in col]
df_x = df[cols]
print(len(cols))
print(df_x)
df_x = df_x.dropna(axis=0, how="all")
sns.heatmap(df_x.isna(), xticklabels=1)
plt.savefig(f'applications_foreground_values', bbox_inches='tight')
df_q = pd.DataFrame()
for col in df_x:
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
sns.heatmap(df_q, cbar=False, xticklabels=1)
plt.savefig(f'cut_applications_foreground_values', bbox_inches='tight')
plt.close()
# battery
cols = [col for col in df.columns if "phone_battery" in col]
df_x = df[cols]
print(len(cols))
print(df_x)
df_x = df_x.dropna(axis=0, how="all")
sns.heatmap(df_x.isna(), xticklabels=1)
plt.savefig(f'phone_battery_values', bbox_inches='tight')
df_q = pd.DataFrame()
for col in df_x:
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
sns.heatmap(df_q, cbar=False, xticklabels=1)
plt.savefig(f'cut_phone_battery_values', bbox_inches='tight')
plt.close()
# bluetooth_doryab
cols = [col for col in df.columns if "bluetooth_doryab" in col]
df_x = df[cols]
print(len(cols))
print(df_x)
df_x = df_x.dropna(axis=0, how="all")
sns.heatmap(df_x.isna(), xticklabels=1)
plt.savefig(f'bluetooth_doryab_values', bbox_inches='tight')
df_q = pd.DataFrame()
for col in df_x:
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
sns.heatmap(df_q, cbar=False, xticklabels=1)
plt.savefig(f'cut_bluetooth_doryab_values', bbox_inches='tight')
plt.close()
# bluetooth_rapids
cols = [col for col in df.columns if "bluetooth_rapids" in col]
df_x = df[cols]
print(len(cols))
print(df_x)
df_x = df_x.dropna(axis=0, how="all")
sns.heatmap(df_x.isna(), xticklabels=1)
plt.savefig(f'bluetooth_rapids_values', bbox_inches='tight')
df_q = pd.DataFrame()
for col in df_x:
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
sns.heatmap(df_q, cbar=False, xticklabels=1)
plt.savefig(f'cut_bluetooth_rapids_values', bbox_inches='tight')
plt.close()
# calls
cols = [col for col in df.columns if "phone_calls" in col]
df_x = df[cols]
print(len(cols))
print(df_x)
df_x = df_x.dropna(axis=0, how="all")
sns.heatmap(df_x.isna(), xticklabels=1)
plt.savefig(f'phone_calls_values', bbox_inches='tight')
df_q = pd.DataFrame()
for col in df_x:
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
sns.heatmap(df_q, cbar=False, xticklabels=1)
plt.savefig(f'cut_phone_calls_values', bbox_inches='tight')
plt.close()
# data_yield
cols = [col for col in df.columns if "data_yield" in col]
df_x = df[cols]
print(len(cols))
print(df_x)
df_x = df_x.dropna(axis=0, how="all")
sns.heatmap(df_x.isna(), xticklabels=1)
plt.savefig(f'data_yield_values', bbox_inches='tight')
df_q = pd.DataFrame()
for col in df_x:
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
sns.heatmap(df_q, cbar=False, xticklabels=1)
plt.savefig(f'cut_data_yield_values', bbox_inches='tight')
plt.close()
# esm
cols = [col for col in df.columns if "phone_esm" in col]
df_x = df[cols]
print(len(cols))
print(df_x)
df_x = df_x.dropna(axis=0, how="all")
sns.heatmap(df_x.isna(), xticklabels=1)
plt.savefig(f'phone_esm_values', bbox_inches='tight')
df_q = pd.DataFrame()
for col in df_x:
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
sns.heatmap(df_q, cbar=False, xticklabels=1)
plt.savefig(f'cut_phone_esm_values', bbox_inches='tight')
plt.close()
# light
cols = [col for col in df.columns if "phone_light" in col]
df_x = df[cols]
print(len(cols))
print(df_x)
df_x = df_x.dropna(axis=0, how="all")
sns.heatmap(df_x.isna(), xticklabels=1)
plt.savefig(f'phone_light_values', bbox_inches='tight')
df_q = pd.DataFrame()
for col in df_x:
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
sns.heatmap(df_q, cbar=False, xticklabels=1)
plt.savefig(f'cut_phone_light_values', bbox_inches='tight')
plt.close()
# locations_doryab
cols = [col for col in df.columns if "locations_doryab" in col]
df_x = df[cols]
print(len(cols))
print(df_x)
df_x = df_x.dropna(axis=0, how="all")
sns.heatmap(df_x.isna(), xticklabels=1)
plt.savefig(f'locations_doryab_values', bbox_inches='tight')
df_q = pd.DataFrame()
for col in df_x:
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
sns.heatmap(df_q, cbar=False, xticklabels=1)
plt.savefig(f'cut_locations_doryab_values', bbox_inches='tight')
plt.close()
# locations_barnett
# Not working
# messages
cols = [col for col in df.columns if "phone_messages" in col]
df_x = df[cols]
print(len(cols))
print(df_x)
df_x = df_x.dropna(axis=0, how="all")
sns.heatmap(df_x.isna(), xticklabels=1)
plt.savefig(f'phone_messages_values', bbox_inches='tight')
df_q = pd.DataFrame()
for col in df_x:
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
sns.heatmap(df_q, cbar=False, xticklabels=1)
plt.savefig(f'cut_phone_messages_values', bbox_inches='tight')
plt.close()
# screen
cols = [col for col in df.columns if "phone_screen" in col]
df_x = df[cols]
print(len(cols))
print(df_x)
df_x = df_x.dropna(axis=0, how="all")
sns.heatmap(df_x.isna(), xticklabels=1)
plt.savefig(f'phone_screen_values', bbox_inches='tight')
df_q = pd.DataFrame()
for col in df_x:
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
sns.heatmap(df_q, cbar=False, xticklabels=1)
plt.savefig(f'cut_phone_screen_values', bbox_inches='tight')
plt.close()
# wifi_visible
cols = [col for col in df.columns if "wifi_visible" in col]
df_x = df[cols]
print(len(cols))
print(df_x)
df_x = df_x.dropna(axis=0, how="all")
sns.heatmap(df_x.isna(), xticklabels=1)
plt.savefig(f'wifi_visible_values', bbox_inches='tight')
df_q = pd.DataFrame()
for col in df_x:
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
sns.heatmap(df_q, cbar=False, xticklabels=1)
plt.savefig(f'cut_wifi_visible_values', bbox_inches='tight')
plt.close()
# All features
print(len(df))
print(df)
# df = df.dropna(axis=0, how="all")
# df = df.dropna(axis=1, how="all")
sns.heatmap(df.isna())
plt.savefig(f'all_features', bbox_inches='tight')
print(df.columns[df.isna().all()].tolist())
print("All NaNs:", df.isna().sum().sum())
print("Df shape NaNs:", df.shape)