def feature_selection(activity_threshold=3):
"""Train classifier on DonorsChoose set given a label to choose most important features.
INPUT:
OUTPUT: list of most important columns
"""
dc_districts = get_donorschoose.districts()
dc_index = dc_districts.index
census = get_census.all_states()
census = census.loc[dc_index].copy()
columns = ["STNAME", "LATCOD", "LONCOD",
"TOTALREV", "TFEDREV", "TSTREV", "TLOCREV", "TOTALEXP", "TCURSSVC", "TCAPOUT",
"Z32", "Z34", "Z35", "HR1", "HE1", "HE2"]
nces = get_nces.districts(columns=columns, nonneg=True)
data = pd.concat([census, nces.loc[census.index]], axis=1)
data.dropna(inplace=True)
label = dc_districts.activity > activity_threshold
label = label.loc[data.index]
print label.value_counts()
feature_importance.importance(data._get_numeric_data(), label)
def feature_selection(activity_threshold=3):
"""Train classifier on DonorsChoose set given a label to choose most important features.
INPUT:
OUTPUT: list of most important columns
"""
dc_districts = get_donorschoose.districts()
dc_index = dc_districts.index
census = get_census.all_states()
census = census.loc[dc_index].copy()
columns = [
"STNAME", "LATCOD", "LONCOD", "TOTALREV", "TFEDREV", "TSTREV",
"TLOCREV", "TOTALEXP", "TCURSSVC", "TCAPOUT", "Z32", "Z34", "Z35",
"HR1", "HE1", "HE2"
]
nces = get_nces.districts(columns=columns, nonneg=True)
data = pd.concat([census, nces.loc[census.index]], axis=1)
data.dropna(inplace=True)
label = dc_districts.activity > activity_threshold
label = label.loc[data.index]
print label.value_counts()
feature_importance.importance(data._get_numeric_data(), label)
def district_similarity():
"""Compute district similarity matrix using census, NCES, and census district data.
OUTPUT: Similarity object
"""
census = get_census.all_states()
columns = ["STNAME", "LATCOD", "LONCOD", "TOTALREV", "TFEDREV", "TSTREV", "TLOCREV", "TOTALEXP", "TCURSSVC", "TCAPOUT", "HR1", "HE1", "HE2"]
nces = get_nces.districts(columns=columns, nonneg=True)
ddf = pd.concat([census, nces.loc[census.index]], axis=1)
sim = similarity.Similarity(ddf, ref_columns=["District Name", "State", "STNAME", "LATCOD", "LONCOD"])
return sim
def district_similarity():
"""Compute district similarity matrix using census, NCES, and census district data.
OUTPUT: Similarity object
"""
census = get_census.all_states()
columns = [
"STNAME", "LATCOD", "LONCOD", "TOTALREV", "TFEDREV", "TSTREV",
"TLOCREV", "TOTALEXP", "TCURSSVC", "TCAPOUT", "HR1", "HE1", "HE2"
]
nces = get_nces.districts(columns=columns, nonneg=True)
ddf = pd.concat([census, nces.loc[census.index]], axis=1)
sim = similarity.Similarity(
ddf,
ref_columns=["District Name", "State", "STNAME", "LATCOD", "LONCOD"])
return sim
