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 potential_districts(sim, n_potential=15, activity_threshold=3):
"""Find potentially active districts outside of DonorsChoose network.
OUTPUT: pandas dataframe of recommended distrcits
districts.topo.json
district.json
"""
dc_districts = get_donorschoose.districts()
active_districts = set(dc_districts[dc_districts.activity > activity_threshold].index.values.astype(np.int))
all_districts = set(sim.data.index.values.astype(np.int))
potential = all_districts - (active_districts & all_districts)
rms = sim.rms_score(potential, active_districts, normalize=True)
# potential districts most similar to active districts in descending order
potential_df = pd.DataFrame(sorted(zip(potential, rms), key=lambda (x, y): y, reverse=True))
potential_df.columns = ["leaid", "score"]
potential_df.index = potential_df.pop("leaid")
potential_df["State"] = sim.data["State"].loc[potential_df.index]
# pick at most n_potential recommendations for each state
recommend = []
for state in sim.data.State.value_counts().index:
recommend.extend(potential_df[potential_df.State == state].head(n_potential).index.values)
rec_df = sim.data[["District Name", "STNAME", "State", "LATCOD", "LONCOD"]].loc[recommend]
rec_df["score"] = potential_df.score.loc[recommend]
N_rec = len(rec_df)
rec_df.dropna(inplace=True)
print "NaNs: drop {} districts".format(N_rec - len(rec_df))
# build tooltip text
district_info = []
for leaid in rec_df.index:
tooltip = []
tooltip.append( "{}".format(rec_df.loc[leaid, "District Name"]) )
tooltip.append( "students: {}".format(sim.data.loc[leaid, "Total Students"].astype(np.int)) )
tooltip.append("")
most_sim = sim.most_similar(leaid)
most_sim.drop(leaid)
most_sim = most_sim.loc[filter(lambda leaid: True if leaid in active_districts else False, most_sim.index)]
closest = most_sim.head(1).index[0]
# same, close = sim.closest_features([leaid, closest])
# closest_features = list(same) + list(close)
tooltip.append( "Most similar to {}, {}".format(most_sim.loc[closest, "District Name"], most_sim.loc[closest, "State"]) )
# tooltip.append( "(based on: {}, {})".format(closest_features[0], closest_features[1]) )
tooltip.append( "students: {}".format(sim.data.loc[closest, "Total Students"].astype(np.int)) )
tooltip.append( "projects: {}".format(dc_districts.loc[closest, "projects"].astype(np.int)) )
donation_per_project = dc_districts.loc[closest, "total_donations"] / dc_districts.loc[closest, "projects"]
tooltip.append( "received donations/project: ${:.2f}".format(donation_per_project) )
htmltooltip = "<br/>".join(tooltip)
district_info.append(htmltooltip)
info_series = pd.Series(district_info)
info_series.index = rec_df.index
rec_df["info"] = info_series
to_geojson(rec_df)
return rec_df
def potential_districts(sim, n_potential=15, activity_threshold=3):
"""Find potentially active districts outside of DonorsChoose network.
OUTPUT: pandas dataframe of recommended distrcits
districts.topo.json
district.json
"""
dc_districts = get_donorschoose.districts()
active_districts = set(dc_districts[
dc_districts.activity > activity_threshold].index.values.astype(
np.int))
all_districts = set(sim.data.index.values.astype(np.int))
potential = all_districts - (active_districts & all_districts)
rms = sim.rms_score(potential, active_districts, normalize=True)
# potential districts most similar to active districts in descending order
potential_df = pd.DataFrame(
sorted(zip(potential, rms), key=lambda (x, y): y, reverse=True))
potential_df.columns = ["leaid", "score"]
potential_df.index = potential_df.pop("leaid")
potential_df["State"] = sim.data["State"].loc[potential_df.index]
# pick at most n_potential recommendations for each state
recommend = []
for state in sim.data.State.value_counts().index:
recommend.extend(potential_df[potential_df.State == state].head(
n_potential).index.values)
rec_df = sim.data[["District Name", "STNAME", "State", "LATCOD",
"LONCOD"]].loc[recommend]
rec_df["score"] = potential_df.score.loc[recommend]
N_rec = len(rec_df)
rec_df.dropna(inplace=True)
print "NaNs: drop {} districts".format(N_rec - len(rec_df))
# build tooltip text
district_info = []
for leaid in rec_df.index:
tooltip = []
tooltip.append("{}".format(rec_df.loc[leaid, "District Name"]))
tooltip.append("students: {}".format(
sim.data.loc[leaid, "Total Students"].astype(np.int)))
tooltip.append("")
most_sim = sim.most_similar(leaid)
most_sim.drop(leaid)
most_sim = most_sim.loc[filter(
lambda leaid: True
if leaid in active_districts else False, most_sim.index)]
closest = most_sim.head(1).index[0]
# same, close = sim.closest_features([leaid, closest])
# closest_features = list(same) + list(close)
tooltip.append("Most similar to {}, {}".format(
most_sim.loc[closest, "District Name"], most_sim.loc[closest,
"State"]))
# tooltip.append( "(based on: {}, {})".format(closest_features[0], closest_features[1]) )
tooltip.append("students: {}".format(
sim.data.loc[closest, "Total Students"].astype(np.int)))
tooltip.append("projects: {}".format(
dc_districts.loc[closest, "projects"].astype(np.int)))
donation_per_project = dc_districts.loc[
closest, "total_donations"] / dc_districts.loc[closest, "projects"]
tooltip.append(
"received donations/project: ${:.2f}".format(donation_per_project))
htmltooltip = "<br/>".join(tooltip)
district_info.append(htmltooltip)
info_series = pd.Series(district_info)
info_series.index = rec_df.index
rec_df["info"] = info_series
to_geojson(rec_df)
return rec_df
