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grid_search_funs.py
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import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import accuracy_score,make_scorer,f1_score,classification_report,average_precision_score
from sklearn.preprocessing import Normalizer,MinMaxScaler,StandardScaler,normalize
from sklearn.cross_validation import train_test_split
import multiprocessing
#############################################
# PANDAS HELPERS
#############################################
def remove_column_from_data_frame(col_to_remove, data_frame):
if col_to_remove in list(data_frame.columns):
data_frame.drop(col_to_remove, axis=1, inplace=True)
def remove_columns_from_data_frame(cols_to_remove, data_frame):
column_dict = {x: None for x in list(data_frame.columns)}
cols_to_remove = [x for x in cols_to_remove if x in column_dict]
data_frame.drop(labels=cols_to_remove, axis=1, inplace=True)
def remove_columns_like(column_pattern, data_frame):
to_remove = [x for x in list(data_frame.columns) if column_pattern in x]
return remove_columns_from_data_frame(to_remove, data_frame)
def fill_nas(value, data_frame):
data_frame.fillna(0, inplace=True)
#############################################
# DATA RETRIEVAL HELPERS
#############################################
def get_data(n_rows=None):
if n_rows is not None:
# df = pd.read_csv('final_feats_without_dummies_3.csv', low_memory=False, nrows=n_rows)
df = pd.read_csv('final_feats_4.csv', low_memory=False, nrows=n_rows)
df_y = pd.read_csv('final_outs_3.csv', low_memory=False, nrows=n_rows)
else:
# df = pd.read_csv('final_feats_without_dummies_3.csv', low_memory=False)
df = pd.read_csv('final_feats_4.csv', low_memory=False)
df_y = pd.read_csv('final_outs_3.csv', low_memory=False)
# Drop labels and a redundant column
# remove_columns_from_data_frame(['Unnamed: 0', 'Unnamed: 0.1' 'dissent', 'dissentdummy'], df)
# df,df_y=remove_bad_rows(df,df_y)
# df=drop_unneeded_cols(df)
# df=drop_dissent(df)
# df=dummify(df)
# Extras -- for analysis
# CASE 1: REMOVE TOP 2
# CASE 2: REMOVE ALL 'DISS'
remove_columns_like('Unnamed', df)
# remove_columns_from_data_frame(['type', 'turnonthresh'], df)
# remove_columns_from_data_frame(['type1', 'last3'], df)
# remove_columns_like('diss', df)
if ('Unnamed: 0' or 'Unnamed: 0.1') in df_y.columns:
df_y.drop(labels=['Unnamed: 0','Unnamed: 0.1'],axis=1,inplace=True)
return df, df_y
def get_x_y(n_rows=None):
df, df_y = get_data(n_rows)
#fill_nas(0, df)
for y in df_y.columns:
if len(pd.unique(df_y.ix[:,y]))==2:
y=df_y.ix[:,y].values
break
return df.values, y
def get_columns():
df, df_y = get_data(1000)
return list(df.columns)
def print_report(y, y_pred):
print classification_report(y, y_pred)
#############################################
# MODEL HELPERS
#############################################
def grid_search(X, y, clf, param_grid, n_jobs=1):
# param_dict={'average': 'weighted'}
scorer = make_scorer(average_precision_score)
gridclf = GridSearchCV(estimator=clf, param_grid=param_grid, scoring=scorer, cv=3, verbose=1, n_jobs=n_jobs)
gridclf.fit(X, y)
return gridclf
def get_top_n_feats(n, feat_arr, cols):
args=np.argsort(feat_arr)
assert len(feat_arr)==len(cols)
col_scores=col_scores=np.array(zip(cols,feat_arr))
return col_scores[args[-n:]].tolist()[::-1]
# def get_top_n(n, arr, col_names, prev_list=[]):
# if n <= 0:
# return []
# most_imp = -1
# most_imp_index = -1
# for i in range(len(arr)):
# if i in prev_list:
# continue
# if arr[i] > most_imp:
# most_imp = arr[i]
# most_imp_index = i
# prev_list.append(most_imp_index)
# return [ (col_names[most_imp_index], most_imp) ] + get_top_n(n - 1, arr, col_names, prev_list)
# In[3]:
def drop_unneeded_cols(df):
del_cols = ['fileid','cite','vol','beginpg','endopin','endpage','docnum','priorpub','_merge','year',
'circuit','pseatno','decision_date','aatty_first_name','aatty_last_name','afirm_name',
'ratty_first_name','ratty_last_name','rname_of_first_listed_amicus_gro','rfirm_namew','decisiondatenew2',
'j1name','j2name','j3name','quartertoelect','pname','seatno','success','lsuc','ls1','ls2','ls3','lp',
'lp2','lp3','sseatno','congress','congreso','afirst_listed_amicus_group','yearquarter','name','Name','State','j',
'codej4','j4vote1','j4vote2','j4maj1','j4maj2','codej5','j5vote1','j5vote2','j5maj1','j5maj2',
'codej6','j6vote1','j6vote2','j6maj1','j6maj2','codej7','j7vote1','j7vote2','j7maj1','j7maj2',
'codej8','j8vote1','j8vote2','j8maj1','j8maj2','codej9','j9vote1','j9vote2','j9maj1','j9maj2',
'codej10','j10vote1','j10vote2','j10maj1','j10maj2','codej11','j11vote1','j11vote2','j11maj1','j11maj2',
'codej12','j12vote1','j12vote2','j12maj1','j12maj2','codej13','j13vote1','j13vote2','j13maj1','j13maj2',
'codej14','j14vote1','j14vote2','j14maj1','j14maj2','codej15','j15vote1','j15vote2','j15maj1','j15maj2','j16maj1','j16vote1']
df.drop(labels=del_cols,axis=1,inplace=True)
moredropcolumns=df.columns.tolist() # .tolist?
for i in moredropcolumns:
if len(pd.unique(df[i]))==1:
df.drop(labels=i,axis=1,inplace=True)
df.drop(labels=['casenum','j2vote1','j2vote2','j2maj1','direct1',
'j2maj2','j3vote1','j3vote2','j3maj1','j3maj2','majvotes','ids'],axis=1,inplace=True)
return df
def dummify(df):
new_cols=df.columns
new_cols=new_cols.tolist()
# keep_cols=['j1score','j2score','j3score','popularpct','electoralpct','closerd','fartherd','dAds3','dF2Ads3',
# 'dF1Ads3','dL1Ads3','dL2Ads3','dL3Ads3','dL4Ads3','dL5Ads3','logAds3','logL1Ads3','logL2Ads3','logF1Ads3',
# 'logF2Ads3','decade2','propneg','likely_elev2','score','d12','d13','d23','sat_together_count']
float_cols=['j1score','j2score','j3score','popularpct','electoralpct','closerd','fartherd','dAds3','dF2Ads3',
'dF1Ads3','dL1Ads3','dL2Ads3','dL3Ads3','dL4Ads3','dL5Ads3','logAds3','logL1Ads3','logL2Ads3','logF1Ads3',
'logF2Ads3','decade2','propneg','likely_elev2','score','d12','d13','d23',
'judgecitations','experience','experiencetrun','age2trun','agego','assets','ba','liable',
'networth','totalcities','sat_together_count','keytotal','lengthopin','Wopinionlenght','Wtotalcites','age']
remove_for_now=['Ads3','F1Ads3','F2Ads3','L1Ads3','L2Ads3','L3Ads3','L4Ads3','L5Ads3','Unnamed: 0.1','appel1','appel2',
'citevol','codej3','id','usc2sect','usc1sect','age2','distjudg','respond1','respond2','yearb','pred','csb']
# df.drop(labels=remove_for_now,inplace=True,axis=1)
for x in remove_for_now:
if x in df.columns:
print "dropped: ",x
df.drop(labels=[x],inplace=True,axis=1)
sum1=0
dummy_cols=[]
for col in df.columns:
if col not in float_cols:
if len(pd.unique(df.ix[:,col]))>100 or (df.ix[:,col].dtype!='float64' and df.ix[:,col].dtype!='int64'):
sum1+= len(pd.unique(df.ix[:,col]))
dummy_cols.append(col)
print "# of dummy columns: ",sum1
print df.shape
print dummy_cols
df2=pd.get_dummies(df,columns=dummy_cols,dummy_na=True,sparse=True)
print df2.shape
df2.fillna(value=0,inplace=True)
return df2
def remove_bad_rows(df_x,df_y):
#remove rows where codej1==codej2
# df[df.codej1==df.codej2].index
same_cols = df_x[df_x.codej1==df_x.codej2].index
df_x=df_x.drop(same_cols).reset_index(drop=True)
df_y=df_y.drop(same_cols).reset_index(drop=True)
#remove rows where >3 judges occur
# pp = pd.read_csv('../raw/Votelevel_stuffjan2013.csv')
# qq=pp.groupby(by=['casenum']).count()
# pd.unique(qq.month)
# rr=qq[qq.month==6].reset_index()
# rr.shape
#remove rows where codej2==null
#df[map(lambda x: not(x),pd.notnull(df.ix[:]["codej2"]).tolist())]
nan_cols=df_x[map(lambda x: not(x),pd.notnull(df_x.ix[:]["codej2"]).tolist())].index
nan_cols.append(df_x[map(lambda x: not(x),pd.notnull(df_x.ix[:]["codej1"]).tolist())].index)
df_x=df_x.drop(nan_cols).reset_index(drop=True)
df_y=df_y.drop(nan_cols).reset_index(drop=True)
return df_x,df_y
def drop_dissent(df,drop=['diss','concur','unan']):
def func(a, b):
return not set(a).isdisjoint(set(b))
diss_list=[]
for col in df.columns:
for x in drop:
if x in col:
diss_list.append(col)
diss_list=list(set(diss_list))
df.drop(labels=diss_list,axis=1,inplace=True)
return df