def create_grouped_index_df(bin_num):
## load the labels and start_time column for train and test data
start_time = time.time()
train_labels = pd.read_csv(data_path + train_num_file, index_col='Id', usecols=['Id', dep_var_name])
train_date_start_columm = pd.read_csv(data_path + train_date_file, index_col='Id', usecols=['Id', start_time_column_name])
test_date_start_columm = pd.read_csv(data_path + test_date_file, index_col='Id', usecols=['Id', start_time_column_name])
end_time = time.time()
print 'data loading takes ', round((end_time - start_time), 1), ' seconds.'
## join the start_time with labels, then drop the NaN in start_time
labeled_start_time = pd.merge(train_labels, train_date_start_columm, how='left', left_index=True, right_index=True)
## this labeled_start_time dataFrame doesn't contain the NaN, therefore it can be directly used for calculating the mquantiles
labeled_start_time = labeled_start_time[~labeled_start_time[start_time_column_name].isnull()]
##section to subset the data by start_time
prob_list = [1.*i/bin_num for i in range(1, bin_num)]
quantile_values = mquantiles(labeled_start_time[start_time_column_name], prob=prob_list)
bins = [labeled_start_time[start_time_column_name].min()]
bins.extend(quantile_values)
bins.append(labeled_start_time[start_time_column_name].max())
bin_names = [str(i) for i in range(len(bins)-1)]
## cut the entire dataframe into different time_windows by start_time
tmp_train = train_date_start_columm.copy()
tmp_test = test_date_start_columm.copy()
tmp_train['time_window_num'] = pd.cut(tmp_train[start_time_column_name], bins, labels=bin_names)
tmp_test['time_window_num'] = pd.cut(tmp_test[start_time_column_name], bins, labels=bin_names)
## create a row number column, start index is 1
tmp_train['row_num'] = range(1, (tmp_train.shape[0] + 1))
tmp_test['row_num'] = range(1, (tmp_test.shape[0] + 1))
return tmp_train, tmp_test, bins, bin_names
def makediscrete2(df, dataType):
dataLabels = list(df.columns.values)
for eachLabel in dataLabels:
if dataType[eachLabel] is 1:
choiceC = True
bins = input('Enter bin size for ' + eachLabel + ": ")
while choiceC is True:
choice = input("Enter \n 1 - for equal size bins \n 2 - for custom range \n You Choice: " )
if choice == 1:
df[eachLabel] = pd.cut(df[eachLabel], bins)
choiceC = False
elif choice == 2:
print ("Enter " + str(bins+1) + " values for bin edges:")
binedges =[]
for x in range(bins+1):
value = input("" + str(x) + ": ")
binedges.append(value)
df[eachLabel] = pd.cut(df[eachLabel], binedges )
choiceC = False
else:
print "Wrong choice Try Again!! "
df.to_csv(Globals.DISCRETIZED_FILE)
print ("continuous data converted to discrete data stored : " + Globals.DISCRETIZED_FILE)
return df, Globals.DISCRETIZED_FILE
def cleaneddf(no_bins=0):
#you'll want to tweak this to conform with your computer's file system
trainpath = '../../data/train.csv'
testpath = '../../data/test.csv'
traindf = pd.read_csv(trainpath)
testdf = pd.read_csv(testpath)
#discretise fare
if no_bins==0:
return [cleandf(traindf), cleandf(testdf)]
traindf=cleandf(traindf)
testdf=cleandf(testdf)
bins_and_binned_fare = pd.qcut(traindf.Fare, no_bins, retbins=True)
bins=bins_and_binned_fare[1]
traindf.Fare = bins_and_binned_fare[0]
testdf.Fare = pd.cut(testdf.Fare, bins)
#discretise age
bins_and_binned_age = pd.qcut(traindf.Age+jitter(traindf.Age), no_bins, retbins=True)
bins=bins_and_binned_age[1]
traindf.Age = bins_and_binned_age[0]
testdf.Age = pd.cut(testdf.Age, bins)
#create a submission file for kaggle
predictiondf = pd.DataFrame(testdf['PassengerId'])
predictiondf['Survived']=[0 for x in range(len(testdf))]
predictiondf.to_csv('./prediction.csv', index=False)
return [traindf, testdf]
def period_by_hours(x, separation):
''' aggrege le x par intervale d'heure.
Le calcul pourrait être simple si on interdisait
le chevauchement de jour.
'''
print(separation)
assert isinstance(separation, list)
assert all([sep < 24 for sep in separation])
separation.sort()
if 0 in separation:
separation.append(24)
hour_categ = pd.cut(x.dt.hour, separation, right=False)
date_categ = x.dt.date
return date_categ.astype(str) + ' ' + hour_categ.astype(str)
else:
hour = x.dt.hour
hour_categ = pd.cut(hour, separation, right=False).astype(str)
night_categ = '[' + str(separation[-1]) + ', ' + str(separation[0]) + ')'
hour_categ[(hour < separation[0]) | (hour >= separation[-1])] = night_categ
assert hour_categ.nunique(dropna=False) == len(separation)
date_categ = x.dt.date.astype(str)
# décalage d'un jour pour les premières heures
decale = x.dt.date[x.dt.hour < separation[1]] + pd.DateOffset(days=-1)
date_categ[x.dt.hour < separation[1]] = decale.astype(str)
assert all(date_categ.str.len() == 10)
return date_categ + ' ' + hour_categ
def binData2D(myXYZ, xstart, xend, ystart, yend, nx, ny):
'''
Fucntion to bin a scatter point cloud (xyz) into a 2d array
:param myXYZ: xyz array containings the point cloud coordiantes
:param xstart:
:param xend:
:param ystart:
:param yend:
:param nx: number of cells along the x-axis
:param ny: number of cells along hte y-axis
:return: a group object (pandas library) with all points classified into bins
'''
# note, the division requires: from _future_ import division
x = myXYZ[:,0].ravel()
y = myXYZ[:,1].ravel()
z = myXYZ[:,2].ravel()
df = pd.DataFrame({'X' : x , 'Y' : y , 'Z' : z})
bins_x = np.linspace(xstart, xend, nx+1)
x_cuts = pd.cut(df.X,bins_x, labels=False)
bins_y = np.linspace(ystart,yend, ny+1)
y_cuts = pd.cut(df.Y,bins_y, labels=False)
bin_xmin, bin_ymin = x_cuts.min(), y_cuts.min()
print('Data cut in a ' + str(bins_x.__len__()) + ' by ' + str(bins_y.__len__()) + ' matrix')
dx = (xend - xstart)/nx
dy = (yend - ystart)/ny
print('dx = ' + str(dx) + ' ; dy = ' + str (dy))
grouped = df.groupby([x_cuts,y_cuts])
print('Data grouped, \nReady to go!!')
return grouped, bins_x, bins_y, int(bin_xmin), int(bin_ymin)
def get_log_odds_chris(target, feature, bins, f_range=None, M=10, display_head=False):
"""return log ( P(feature=x | target=1) / P(feature=x | target=0) )
tn : targent name, 0 or 1
fn : x name
f_range : x의 범위 제한
M : smoothing factor
"""
tn = target.name
fn = feature.name
X = pd.concat([target, feature], axis=1)
if f_range is not None:
X = X[(X[fn] > f_range[0]) & (X[fn] < f_range[1])]
if display_head:
X["_cut"] = pd.cut(X[fn], bins=bins).astype(str)
X["_cut"] = X._cut.map(lambda x: float(x.split(",")[0][1:]))
else:
X["_cut"] = pd.cut(X[fn], bins=bins)
Y = X.groupby("_cut").apply(
lambda x: np.log((x[tn].sum() + 1.0 * M / bins) / ((1.0 - x[tn]).sum() + 1.0 * M / bins))
)
# display(X.groupby('_cut').apply(lambda x: (x[tn].sum(), (1-x[tn]).sum())))
# display(Y)
Y = Y - np.log((1.0 * X[tn].sum() + M) / ((1.0 - X[tn]).sum() + M))
Y = pd.DataFrame(Y, columns=["%s_log_odds" % fn])
return Y
def make_object_map(data,field,**kwargs): linear = False for key,value in kwargs.iteritems(): if key == 'linear': linear = value print linear if linear == False: colors,rangelist = make_distributed_range(data,field) else: colors = get_heatmap51() colors2 = colors maxvalue = data[field].max() if maxvalue < 51: totallist = range(maxvalue) colors = reduce_color_list_size(totallist,colors) colors,rangelist = make_gradient_range(data[field].min(),maxvalue,colors) else: colors = reduce_color_list_size(range(len(data)),colors) colors,rangelist = make_gradient_range(data[field].min(),maxvalue,colors) if not rangelist[0] == 0: rangelist = [0] + rangelist[1:] data['COLORKEY'] = pd.cut(data[field],bins=rangelist+[1000000000],labels=colors) return data colors2 = get_heatmap51() if not rangelist[0] == 0: rangelist = [0] + rangelist[1:] data['COLORKEY'] = pd.cut(data[field],bins=rangelist,labels=colors[1:]) return data
def get_indicators(start_date, end_date, symbols):
"""Simulate and assess the performance of a stock portfolio."""
# Read in adjusted closing prices for given symbols, date range
dates = pd.date_range(start_date, end_date)
prices_all = get_data(symbols, dates) # automatically adds SPY
prices = prices_all[symbols] # only portfolio symbols
# prices_SPY = prices_all['SPY'] # only SPY, for comparison later
sym = symbols[1]
x1 = (prices[sym] - pd.rolling_mean(prices[sym], 20)) / (2 * pd.rolling_std(prices[sym], 20))
x1_dis = pd.cut(x1, 10, labels=False)
x2 = prices[sym].pct_change(20)
x2_dis = pd.cut(x2, 10, labels=False)
x3 = pd.rolling_std(prices[sym].pct_change(1), 20)
x3_dis = pd.cut(x3, 10, labels=False)
# return pd.concat([x1_,x2_0,x3_0], axis=1).dropna(), prices
tempdf = pd.concat([x1_dis, x2_dis, x3_dis], axis=1).dropna()
tempdf.columns = ["x1", "x2", "x3"]
print tempdf.dtypes
tempdf["holding"] = np.random.randint(0, 3, size=len(tempdf))
# 0 = no position , 1 = negative positin 2 =holding long
tempdf["s"] = 1000 * tempdf["holding"] + 100 * tempdf["x3"] + 10 * tempdf["x2"] + 1 * tempdf["x1"]
print tempdf.head(50)
return tempdf, prices
def test_bins_not_overlapping_from_interval_index():
# see gh-23980
msg = "Overlapping IntervalIndex is not accepted"
ii = IntervalIndex.from_tuples([(0, 10), (2, 12), (4, 14)])
with pytest.raises(ValueError, match=msg):
cut([5, 6], bins=ii)
def test_cython_agg_empty_buckets_nanops(observed):
# GH-18869 can't call nanops on empty groups, so hardcode expected
# for these
df = pd.DataFrame([11, 12, 13], columns=['a'])
grps = range(0, 25, 5)
# add / sum
result = df.groupby(pd.cut(df['a'], grps),
observed=observed)._cython_agg_general('add')
intervals = pd.interval_range(0, 20, freq=5)
expected = pd.DataFrame(
{"a": [0, 0, 36, 0]},
index=pd.CategoricalIndex(intervals, name='a', ordered=True))
if observed:
expected = expected[expected.a != 0]
tm.assert_frame_equal(result, expected)
# prod
result = df.groupby(pd.cut(df['a'], grps),
observed=observed)._cython_agg_general('prod')
expected = pd.DataFrame(
{"a": [1, 1, 1716, 1]},
index=pd.CategoricalIndex(intervals, name='a', ordered=True))
if observed:
expected = expected[expected.a != 1]
tm.assert_frame_equal(result, expected)
def get_data_frame_with_dummies(users):
users_ref = users.copy()
base_dummies = None
categories = {'gender': ['male', 'female'], 'education': ['overGraduate', 'university', 'underHigh'],
'income': ['100', '200', '300', '400', '500', '1200more'], 'job': ['officer', 'student', 'etc'],
'marriage': ['married', 'single'], 'religion': ['buddhist', 'none', 'christian', 'romanCatholicism']}
age_bins = [10, 20, 30, 40, 50, 60, 70]
numChild_bins = [0, 1, 10]
for label_type in users_ref.columns:
temp_dummies = None
if label_type == 'age':
temp_dummies = pd.get_dummies(pd.cut(users_ref[label_type], age_bins, right=False), prefix=label_type)
elif label_type == 'numberOfChildren':
temp_dummies = pd.get_dummies(pd.cut(users_ref[label_type], numChild_bins, right=False), prefix=label_type)
elif label_type == 'residence':
continue
else:
users_ref[label_type + "_cat"] = pd.Categorical(users_ref[label_type],
categories=categories.get(label_type))
temp_dummies = pd.get_dummies(users_ref[label_type + "_cat"], prefix=label_type)
if base_dummies is None:
base_dummies = temp_dummies
else:
base_dummies = pd.concat([base_dummies, temp_dummies], axis=1)
label_nums = base_dummies.sum()
label_rates = label_nums / float(len(users_ref))
return base_dummies, label_nums, label_rates
def test_datetime_cut(self):
# GH 14714
# testing for time data to be present as series
data = to_datetime(Series(['2013-01-01', '2013-01-02', '2013-01-03']))
result, bins = cut(data, 3, retbins=True)
expected = (
Series(IntervalIndex([
Interval(Timestamp('2012-12-31 23:57:07.200000'),
Timestamp('2013-01-01 16:00:00')),
Interval(Timestamp('2013-01-01 16:00:00'),
Timestamp('2013-01-02 08:00:00')),
Interval(Timestamp('2013-01-02 08:00:00'),
Timestamp('2013-01-03 00:00:00'))]))
.astype(CDT(ordered=True)))
tm.assert_series_equal(result, expected)
# testing for time data to be present as list
data = [np.datetime64('2013-01-01'), np.datetime64('2013-01-02'),
np.datetime64('2013-01-03')]
result, bins = cut(data, 3, retbins=True)
tm.assert_series_equal(Series(result), expected)
# testing for time data to be present as ndarray
data = np.array([np.datetime64('2013-01-01'),
np.datetime64('2013-01-02'),
np.datetime64('2013-01-03')])
result, bins = cut(data, 3, retbins=True)
tm.assert_series_equal(Series(result), expected)
# testing for time data to be present as datetime index
data = DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03'])
result, bins = cut(data, 3, retbins=True)
tm.assert_series_equal(Series(result), expected)
def bootstrapped_utility(df_testing=None,varname=None, bins = None):
N = 1500
MKT = 500000
S_bin = 6000
L_bin = 3000
IT_bin = 0.20
IP_bin = 0.40
df_bs = df_testing.sample(frac=1, replace=True)
df_bs_good = df_bs[df_bs['is_good']==1]
df_bs_bad = df_bs[df_bs['is_good']!=1]
hbs_good = cut(df_bs_good[varname], bins=bins)
hbs_bad = cut(df_bs_bad[varname], bins=bins)
bs_purity_by_bin = []
bs_efficiency_by_bin = []
for g in range(0,len(hbs_good.value_counts())):
sum_g_b = hbs_good.value_counts()[g] + hbs_bad.value_counts()[g]
if sum_g_b !=0:
bs_purity_by_bin.append(1.0*hbs_good.value_counts()[g]/sum_g_b)
else:
bs_purity_by_bin.append(1.0*hbs_good.value_counts()[g])
bs_efficiency_by_bin.append(1.0*sum_g_b/(hbs_good.size+hbs_bad.size))
bs_purity_by_bin = array(bs_purity_by_bin)
bs_default_by_bin = -1*(bs_purity_by_bin - 1.0)
bs_efficiency_by_bin = array(bs_efficiency_by_bin)
bs_DC_bin = N * bs_efficiency_by_bin * bs_default_by_bin * L_bin
bs_RT_bin = N * bs_efficiency_by_bin * bs_purity_by_bin * S_bin * IT_bin
bs_RP_bin = N * bs_efficiency_by_bin * bs_default_by_bin * (S_bin - L_bin) * IP_bin
bs_f_bin = bs_RT_bin + bs_RP_bin - bs_DC_bin
bs_f = cumsum(bs_f_bin[::-1])[::-1] - MKT
return bs_f
def cleaneddf(no_bins=0):
#you'll want to tweak this to conform with your computer's file system
testpath = r'C:\Pradeep\Working Set\Consulting\Kaggle\Titanic\Data Sets Oirig\test.csv'
trainpath = r'C:\Pradeep\Working Set\Consulting\Kaggle\Titanic\Data Sets Oirig\train.csv'
print trainpath
traindf = pd.read_csv(trainpath)
testdf = pd.read_csv(testpath)
#discretise fare
if no_bins==0:
return [cleandf(traindf), cleandf(testdf)]
traindf=cleandf(traindf)
testdf=cleandf(testdf)
bins_and_binned_fare = pd.qcut(traindf.Fare, no_bins, retbins=True)
bins=bins_and_binned_fare[1]
traindf.Fare = bins_and_binned_fare[0]
testdf.Fare = pd.cut(testdf.Fare, bins)
#discretise age
bins_and_binned_age = pd.qcut(traindf.Age, no_bins, retbins=True)
bins=bins_and_binned_age[1]
traindf.Age = bins_and_binned_age[0]
testdf.Age = pd.cut(testdf.Age, bins)
#create a submission file for kaggle
predictiondf = pd.DataFrame(testdf['PassengerId'])
predictiondf['Survived']=[0 for x in range(len(testdf))]
predictiondf.to_csv(r'C:\Pradeep\Working Set\Consulting\Kaggle\Titanic\Data Sets Oirig\prediction.csv',
index=False)
return [traindf, testdf]
def cleaneddf(no_bins=0):
#you'll want to tweak this to conform with your computer's file system
trainpath = 'C:/Documents and Settings/DIGIT/My Documents/Google Drive/Blogs/triangleinequality/Titanic/rawtrain.csv'
testpath = 'C:/Documents and Settings/DIGIT/My Documents/Google Drive/Blogs/triangleinequality/Titanic/rawtest.csv'
traindf = pd.read_csv(trainpath)
testdf = pd.read_csv(testpath)
#discretise fare
if no_bins==0:
return [cleandf(traindf), cleandf(testdf)]
traindf=cleandf(traindf)
testdf=cleandf(testdf)
bins_and_binned_fare = pd.qcut(traindf.Fare, no_bins, retbins=True)
bins=bins_and_binned_fare[1]
traindf.Fare = bins_and_binned_fare[0]
testdf.Fare = pd.cut(testdf.Fare, bins)
#discretise age
bins_and_binned_age = pd.qcut(traindf.Age, no_bins, retbins=True)
bins=bins_and_binned_age[1]
traindf.Age = bins_and_binned_age[0]
testdf.Age = pd.cut(testdf.Age, bins)
#create a submission file for kaggle
predictiondf = pd.DataFrame(testdf['PassengerId'])
predictiondf['Survived']=[0 for x in range(len(testdf))]
predictiondf.to_csv('C:/Documents and Settings/DIGIT/My Documents/Google Drive/Blogs/triangleinequality/Titanic/prediction.csv',
index=False)
return [traindf, testdf]
def dummies_xy(cls, order_has_ret):
"""
bins的选择不是胡来,根据binscs可视化数据结果进行
:param order_has_ret:
:return:
"""
bins = [-np.inf, 0.0, 0.1, 0.2, 0.4, 0.50, 0.85, 1.0, 1.2, np.inf]
cats = pd.cut(order_has_ret.wave_score1, bins)
wave_score1_dummies = pd.get_dummies(cats, prefix='ws1_dummies')
order_has_ret = pd.concat([order_has_ret, wave_score1_dummies], axis=1)
bins = [-np.inf, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0, np.inf]
cats = pd.cut(order_has_ret.atr_std, bins)
atr_dummies = pd.get_dummies(cats, prefix='atr_dummies')
order_has_ret = pd.concat([order_has_ret, atr_dummies], axis=1)
bins = [-np.inf, -20, -12, -7, -3, 0, 3, 7, 12, 20, np.inf]
cats = pd.cut(order_has_ret.deg_hisWindowPd, bins)
deg_his_window_dummies = pd.get_dummies(cats, prefix='dh_dummies')
order_has_ret = pd.concat([order_has_ret, deg_his_window_dummies], axis=1)
cats = pd.cut(order_has_ret.deg_windowPd, bins)
deg_window_dummies = pd.get_dummies(cats, prefix='dw_dummies')
order_has_ret = pd.concat([order_has_ret, deg_window_dummies], axis=1)
cats = pd.cut(order_has_ret.deg_60WindowPd, bins)
deg_60window_dummies = pd.get_dummies(cats, prefix='d60_dummies')
order_has_ret = pd.concat([order_has_ret, deg_60window_dummies], axis=1)
return order_has_ret
def psi(bench,target,group,print_df = True):
""" This function return the Population Stability Index, quantifying if the
distribution is stable between two states.
This statistic make sense and works is only working for numeric variables
for bench and target.
Params:
- bench is a numpy array with the reference variable.
- target is a numpy array of the new variable.
- group is the number of group you want consider.
"""
labels_q = np.percentile(bench,[(100.0/group)*i for i in range(group + 1)],interpolation = "nearest")
# This is the right approach when you have not a lot of unique value
ben_pct = (pd.cut(bench,bins = np.unique(labels_q),include_lowest = True).value_counts())/len(bench)
target_pct = (pd.cut(target,bins = np.unique(labels_q),include_lowest = True).value_counts())/len(target)
target_pct = target_pct.sort_index()# sort the index
ben_pct = ben_pct.sort_index() # sort the index
psi = sum((target_pct - ben_pct)*np.log(target_pct/ben_pct))
# Print results for better understanding
if print_df:
results = pd.DataFrame({'ben_pct': ben_pct.values,
'target_pct': target_pct.values},
index = ben_pct.index)
return {'data':results,'statistic': psi}
return psi
def print_wt_dist(df):
age_breaks = [0, 19, 29, 39, 49, 59, 69, 79, 100]
df['age_band'] = pd.cut(df['age'], age_breaks)
df['weight_band'] = pd.cut(df['weight'], weight_breaks)
pt = pd.pivot_table(df, index=['weight_band'],
columns=['sex', 'age_band'], values=['age'], aggfunc=[len])
print 1. * pt.cumsum(0) / pt.sum()
def Categoricalize(df, feats):
""" Turns the continuous raw data into categorical data. Specific to the
current setup of features for the NS2 data
Arguments:
df - pandas.DataFrame contatining the data to categoricalize
feats - List of features in the DataFrame
"""
# I chose this, no idea how good it is
time_cuts = np.array([0,1,2,3,4,5,7.5,10,12.5,15,20,25,30,40,50,60,90,120])
#time_cuts = np.array([0,1,2,5,10,15,30,60,120])
time_cuts *= 60
# THIS IS SPECIFIC TO THE CURRENT DATA SETUP
time_feats = feats[:1] + feats[5:-1]
for feat in time_feats:
df[feat]=pd.cut(df[feat],time_cuts)
tvt_feats = feats[1:5]
tvt_cuts = np.array([0, 0.1, 0.2, 0.25, 0.333, 0.5, 0.667, 0.75, 1, 1.333, 1.5, 2, 3, 4, 5, 10, 10000])
#tvt_cuts = np.array([0, 0.1, 0.2, 0.5, 1, 2, 5, 10, 10000])
for feat in tvt_feats:
df[feat]=pd.cut(df[feat],tvt_cuts)
def compress_matrix(matrix, nrows=None, ncols=None):
'''Compress a matrix to a new number of rows/columns. Cells
in the matrix are collapsed by averaging. Assumes matrix is sorted
Parameters
----------
matrix : pandas.DataFrame
matrix to be compressed. Index(s) must be numeric.
nrows, nocls : int
Number of rows/columns in the output matrix. If ``None`` then will
be same as input matrix.
Returns
-------
pandas.DataFrame
If `nrows`/`ncols` is ``None`` then the corresponding index will
be unchanged. Otherwise, index will be integer 0 to `nrows`/`ncols`
.
'''
if ncols:
groups, bins = pd.cut(matrix.columns.values, ncols,
retbins=True, labels=False)
groups = bins[groups]
matrix = matrix.groupby(groups, axis=1).mean()
if nrows:
groups = pd.cut(range(matrix.shape[0]), nrows, labels=False)
matrix = matrix.groupby(groups).mean()
return matrix
def add_digitized_columns_given_input_filter(df_orig, columns_list, cut_point_list, based_on_filter, quantile_cut_point_format= True,
digitized_columns_names=[]):
df = df_orig.copy()
filter_name = '*'.join(['_Digital'] +based_on_filter)
if not digitized_columns_names:
digitized_columns_names = map(lambda x: x + filter_name, columns_list)
print digitized_columns_names
if not based_on_filter:
if quantile_cut_point_format:
for k,col in enumerate(columns_list):
df[digitized_columns_names[k]] = cut_modified(df[col], cut_point_list )
else:
for k,col in enumerate(columns_list):
df[digitized_columns_names[k]] = pd.cut(df[col], cut_point_list, labels =range(len(cut_point_list)-1))
else:
df_groups = df.groupby(based_on_filter)
if quantile_cut_point_format:
for k,col in enumerate(columns_list):
#df[digitized_columns_names[k]] = df_groups[col].transform(lambda x: pd.qcut(x,cut_point_list,
#labels =digital_vals))
df[digitized_columns_names[k]] = df_groups[col].transform(lambda x: cut_modified(x,cut_point_list))
else:
for k,col in enumerate(columns_list):
df[digitized_columns_names[k]] = df_groups[col].transform(lambda x: pd.cut(x,cut_point_list,
labels =range(len(cut_point_list)-1)))
return df, digitized_columns_names
def one2two(file_in=PATH_FILE_OUT, file_out=PATH_FILE_FINAL):
data = pd.read_pickle(file_in)['close']
data = data.reshape(-1, 24)
data = np.array([data[i:i + 24] for i in range(data.shape[0] - 24 + 1)])
data_s = {
'open_price': np.array([data[i][0][0]
for i in range(data.shape[0] - 1)]),
'close_price': np.array([data[i][int(NUM_PIX / 24) - 1][23]
for i in range(data.shape[0] - 1)]),
'max_price': np.array([data[i].max()
for i in range(data.shape[0] - 1)]),
'min_price': np.array([data[i].min()
for i in range(data.shape[0] - 1)]),
'mean_price': np.array([data[i].mean()
for i in range(data.shape[0] - 1)]),
'median_price': np.array([np.median(data[i])
for i in range(data.shape[0] - 1)]),
'buy_or_sell': np.array(
[int(data[i + 1][int(NUM_PIX / 24) - 1][23] > data[i + 1][0][0])
for i in range(data.shape[0] - 1)]),
'change': np.array(
[(data[i + 1][int(NUM_PIX / 24) - 1][23] - data[i + 1][0][0]) /
data[i + 1][int(NUM_PIX / 24) - 1][23] * 100
for i in range(data.shape[0] - 1)])}
data_s = pd.DataFrame(data_s)
bins = [-100, -5, -4, -3, -2, -1.5, -1, -
0.5, 0, 0.5, 1, 1.5, 2, 3, 4, 5, 100]
labels = [-8, -7, -6, -5, -4, -3, -2, -1, 1, 2, 3, 4, 5, 6, 7, 8]
data_s['change_D_16'] = pd.cut(data_s['change'], bins, labels=labels)
bins = [-100, -5, -2, 0, 2, 5, 100]
labels = [-3, -2, -1, 1, 2, 3]
data_s['change_D'] = pd.cut(data_s['change'], bins, labels=labels)
data = data.reshape(len(data), NUM_PIX)
np.save(file_out[0], data[:len(data) - 1])
data_s.to_pickle(file_out[1])
def cleaneddf(no_bins=0):
trainpath = 'Titanic/train.csv'
testpath = 'Titanic/test.csv'
traindf = pd.read_csv(trainpath)
testdf = pd.read_csv(testpath)
#discretise fare
if no_bins == 0:
return [cleandf(traindf), cleandf(testdf)]
traindf = cleandf(traindf)
testdf = cleandf(testdf)
bins_and_binned_fare = pd.qcut(traindf.Fare, no_bins, retbins = True)
bins = bins_and_binned_fare[1]
traindf.Fare = bins_and_binned_fare[0]
testdf.Fare = pd.cut(testdf.Fare, bins)
#discrete age
bins_and_binned_age = pd.qcut(traindf.Age, no_bins, retbins = True)
bins = bins_and_binned_age[1]
traindf.Age = bins_and_binned_age[0]
testdf.Age = pd.cut(testdf.Age, bins)
#create a file for kaggle
predictiondf = pd.DataFrame(testdf['PassengerId'])
predictiondf['Survived']=[0 for x in range(len(testdf))]
predictiondf.to_csv('Titanic/prediction.csv', index = False)
return [traindf, testdf]
def make_continuous_bar_source(df, x_field, y_field='None', df_orig=None, agg='count'):
"""Makes discrete, then creates representation of the bars to be plotted.
Args:
df (DataFrame): contains the data to be converted to a discrete form
x_field (str): the column in df that maps to the x dim of the plot
y_field (str, optional): the column in df that maps to the y dim of the plot
df_orig (DataFrame, optional): original dataframe that the subset ``df`` was
generated from
agg (str, optional): the type of aggregation to be used
Returns:
ColumnDataSource: aggregated, discrete form of x,y values
"""
# Generate dataframe required to use the categorical bar source function
idx, edges = pd.cut(x=df[x_field], bins=8, retbins=True, labels=False)
labels, edges = pd.cut(x=df[x_field], bins=8, retbins=True)
centers = pd.rolling_mean(edges, 2)[1:]
# store new value of x as the bin it fell into
df['centers'] = centers[idx]
df['labels'] = labels
# After making it discrete, create the categorical bar source
return make_categorical_bar_source(df, 'labels', y_field, df_orig, agg)
def calc_p_values(data, gt1_name, gt2_name,
stat_colname=None,
num_bins=50, bin_how='mean',
):
if stat_colname is None:
raise ValueError("you must explicitly set stat_colname (try 'maxWingAngle')")
data.index = data.index.astype(np.int64) #LAZY DANNO. DROP TIMESTAMPS FOR BINNING.
data['synced_ns'] = data.index
df_ctrl = data[data.group == gt1_name][['FlyID', stat_colname, 'synced_ns']]
df_exp = data[data.group == gt2_name][['FlyID', stat_colname, 'synced_ns']]
align_start = df_ctrl.index.min()
dalign = df_ctrl.index.max() - align_start
p_values = DataFrame()
if bin_how=='mean':
bin_func = np.mean
elif bin_how=='median':
bin_func = np.median
bins = np.linspace(0,dalign,num_bins+1) + align_start
binned_ctrl = pd.cut(df_ctrl.index, bins, labels= bins[:-1])
binned_exp = pd.cut(df_exp.index, bins, labels= bins[:-1])
for x in binned_ctrl.levels:
test1_full_dataset = df_ctrl[binned_ctrl == x]
test2_full_dataset = df_exp[binned_exp == x]
bin_start_time = test1_full_dataset['synced_ns'].min()
bin_stop_time = test1_full_dataset['synced_ns'].max()
test1 = []
for obj_id, fly_group in test1_full_dataset.groupby('FlyID'):
test1.append( bin_func(fly_group[stat_colname].values) )
test1 = np.array(test1)
test2 = []
for obj_id, fly_group in test2_full_dataset.groupby('FlyID'):
test2.append( bin_func(fly_group[stat_colname].values) )
test2 = np.array(test2)
try:
hval, pval = kruskal(test1, test2)
except ValueError as err:
pval = 1.0
dftemp = DataFrame({'Bin_number': x,
'P': pval,
'bin_start_time':bin_start_time,
'bin_stop_time':bin_stop_time,
'name1':gt1_name,
'name2':gt2_name,
'test1_n':len(test1),
'test2_n':len(test2),
}, index=[x])
p_values = pd.concat([p_values, dftemp])
return p_values
def make_color_grouped_scatter_plot(data_frame, x_name, y_name, color_by, filename, colormap, x_function = 'dummy', y_function = 'dummy', color_function = 'dummy', legend = False, colorbar = True):
### Originally created for issue_21
def dummy(a): return a
data_frame = data_frame.copy()
p = Ppl(colormap, alpha=1)
fig, ax = plt.subplots(1)
#ax.set_autoscale_on(False)
ax.set_xlim([eval(x_function)(min(data_frame[x_name])), eval(x_function)(max(data_frame[x_name]))])
ax.set_ylim([eval(y_function)(min(data_frame[y_name])), eval(y_function)(max(data_frame[y_name]))])
x_label = x_name.capitalize().replace('_', ' ')
if x_function == 'log': x_label += ' (log)'
y_label = y_name.capitalize().replace('_', ' ')
if y_function == 'log': y_label += ' (log)'
ax.set_xlabel(x_label)
ax.set_ylabel(y_label)
ax.xaxis.get_major_formatter().set_powerlimits((0, 1))
ax.yaxis.get_major_formatter().set_powerlimits((0, 1))
# Show the whole color range
n_intervals = len(colormap.colors)
if color_function == 'log': bins = np.logspace(np.log10( data_frame[color_by].min()), np.log10(data_frame[color_by].max()), n_intervals + 1, base = 10)
else: bins = np.linspace(eval(color_function)(data_frame[color_by].min()), eval(color_function)(data_frame[color_by].max()), n_intervals + 1)
data_frame['groups'] = pandas.cut(data_frame[color_by], bins=bins, labels = False)
groups = pandas.cut(data_frame[color_by], bins=bins)
bounds = []
for g in range(n_intervals):
x = eval(x_function)(data_frame[data_frame.groups == g][x_name])
y = eval(y_function)(data_frame[data_frame.groups == g][y_name])
p.scatter(ax, x, y, label=str(groups.levels[g]), s = 5, linewidth=0)
if legend: p.legend(ax, loc=0)
#ax.set_title('prettyplotlib `scatter` example\nshowing default color cycle and scatter params')
bounds = bins
if colorbar:
cmap = p.get_colormap().mpl_colormap
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
#ax2.set_ylabel(color_by.capitalize().replace('_', ' '), rotation='horizontal')
#ax2.xaxis.get_major_formatter().set_powerlimits((0, 1))
#ax2.yaxis.get_major_formatter().set_powerlimits((0, 1))
ax2 = fig.add_axes([0.9, 0.1 , 0.03, 0.8])
cbar = mpl.colorbar.ColorbarBase(ax2, cmap=cmap, spacing='proportional', ticks=bounds, norm=norm, alpha=1, orientation='vertical')
#cbar.ax.set_xticklabels(map(lambda x: '%.3g'%x, bounds))# vertically oriented colorbar
cbar.ax.set_yticklabels([])# vertically oriented colorbar
#for j, lab in enumerate(map(lambda lower, upper: '%.3g~%.3g'%(lower, upper), bounds[:-1], bounds[1::])):
cbar.ax.text(0,1.02, '%.3g'%max(map(eval(color_function), bounds)))
#for j, lab in enumerate(map(lambda upper: '< %.3g'%upper, bounds[1::])):
# cbar.ax.text(.5, (2 * j + 1) / 8.0, lab, ha='center', va='center', rotation='vertical')
#cbar.ax.set_xticklabels([str(int(t)) for t in bounds])# vertically oriented colorbar
if color_function == 'log': label = color_by.capitalize().replace('_', ' ') + ' (log)'
else: label = color_by.capitalize().replace('_', ' ')
cbar.ax.set_ylabel(label, rotation='vertical')
fig.savefig(filename)
return ax, fig
def twoway(df=df):
df['num_years_bucket'] = pd.cut(df['num_years'], bins=range(0,31,5), right=False,\
retbins=False)
df['num_moves_bucket'] = pd.cut(df['num_moves'], bins=range(0,31,5), right=False,\
retbins=False)
output = pd.DataFrame(df.groupby(['num_moves_bucket', 'num_years_bucket']).size()).reset_index()
output = output.pivot('num_moves_bucket', 'num_years_bucket', 0)
return output
def binned_color_code_return_probability(train, test, deviations=1.0) -> (
pd.DataFrame, pd.DataFrame):
"""Bin the colorCode column in training and test using return probabilities of training data.
Notes
-----
This is to deal with unknown colorCodes (CC's) in the target set by binning the CC range.
The binning considers outlier CC's by keeping them separate, 1-sized bins.
Outliers are CC's whose return probability is over one standard deviation away from the mean.
For our training data (mean: 0.548, std: 0.114) colorCode c is an is an outlier if
retProb(c) < 0.434 || 0.662 < retProb(c), given deviations = 1.
Parameters
----------
train : pd.DataFrame
test : pd.DataFrame
See apply_return_probs
deviations : float
Number of standard deviations a return probability has to differ from the mean to be
considered an outlier.
"""
color_code_min = 0
color_code_max = 9999
# Calculate return probability for each colorCode
color_code_ret_probs = (train
.groupby('colorCode')['returnQuantity']
.apply(group_return_probability))
# Reindex those values to resemble to distribution in the training set
row_ret_probs = color_code_ret_probs.reindex(train['colorCode'])
# Calculate mean and minimum mean distance
mean = row_ret_probs.mean()
diff = row_ret_probs.std() * deviations
mean_distances = color_code_ret_probs.sub(mean).abs()
# iterate over colorCodes and respective mean distances to collect bins
bins = [color_code_min]
for cc, mean_distance in mean_distances.items():
if mean_distance > diff:
# add the colorCode as 1-sized bin (current cc and cc + 1)
# if the last colorCode was added, don't add the current cc, only cc + 1.
if bins[-1] != cc:
bins.append(cc)
bins.append(cc + 1)
bins.append(color_code_max + 1)
# Assign bins to each row in test and training data
train['binnedColorCode'] = pd.cut(train['colorCode'], bins, right=False, labels=False)
test['binnedColorCode'] = pd.cut(test['colorCode'], bins, right=False, labels=False)
train, test = apply_return_probs(train, test, 'binnedColorCode', 'colorReturnProb')
# Test set colorCodes that are bigger than any colorCode in the training set fall into a
# category that has no returnProbability. Impute that bin with the mean retProb.
test['colorReturnProb'] = test['colorReturnProb'].fillna(mean)
return train, test
def reduce_data_to_field(self, data, xkey='x', ykey='y',
reducer=np.median, num_bins=1, **kwargs):
"""Take data and bin by two of its coordinates (default focal plane
coordinates x and y). Then, in each bin, apply the reducer function.
Parameters
----------
data : dataframe
Pandas dataframe that has xkey and ykey columns.
xkey, ykey : strings, default 'x' and 'y'
Keys by which the data are binned. These keys must be in the data,
or else problems will arise!
reducer : function
Function that takes set of data and returns a number. After the
data is binned, one applies this function to the data in a bin. So
if one sets reducer=np.mean, then the resultant data is a two
dimensional histogram.
num_bins : int, default 1
Number of bins for the focal plane. If less than six, then the
number of bins is a sort of proxy for the number of divisions of a
chip. Default is 2 bins per chip. This implies that num_bins < 6
does not really make sense for xkey,ykey neq x,y
Returns
-------
field : dataframe
Dataframe binned by x and y coordinates.
bins_x, bins_y : arrays
Arrays of the bins used.
Notes
-----
data needs to have 'x' and 'y' keys!
"""
x = data[xkey]
y = data[ykey]
if num_bins < 6:
bins_x, bins_y = self.edges(num_bins)
else:
bins_x = np.linspace(np.min(x), np.max(x), num_bins)
bins_y = np.linspace(np.min(y), np.max(y), num_bins)
groups = data.groupby([pd.cut(x, bins_x), pd.cut(y, bins_y)])
field = groups.aggregate(reducer)
# also get the count
counts = groups[xkey].aggregate('count').values
# filter out nanmins on x and y
field = field[field[xkey].notnull() & field[xkey].notnull()]
# counts already filtered out notnull so let's try!
field['N'] = counts
return field, bins_x, bins_y
def run_test2():
orig_animals = ['cat', 'dog', 'mouse']
animals = orig_animals * 3
raw_data = { 'animal' : animals,
'score' : get_rand_num_array(len(animals))
}
# make DataFrame
#
df = pd.DataFrame(raw_data, columns = ['animal', 'score'])
print '-' * 10
print df
print '-' * 10
#return
# Create array for bins
#
bins = get_bin_list(step=20, low_num=0, high_num=100)
# For each score assign it to a bin
#
labels = pd.cut(df['score'], bins)
# Same as above but adding the bin value as a column to the DataFrame
#
df['bin_label'] = pd.cut(df['score'], bins)
print type(df)
print df.describe
print '-' * 10
from collections import Counter
c = Counter(df['bin_label'])
print '-' * 10
print c
vcounts = pd.value_counts(df['bin_label'])
print vcounts
#print 'by_bin', by_bin
print '-' * 10
vcounts = df['bin_label'].value_counts()
d = vcounts.to_dict()
keys = d.keys()
keys.sort()
for k in keys:
print k, d[k], type(k)
return
# Show the count in each bin
#
vc_series = pd.value_counts(df['bin_label'])
print '\n', 'vc_series', vc_series
print '-' * 10
print vc_series.axes
import ipdb; ipdb.set_trace()
payer_enc['START']).dt.days >= 21]
#----- Calculate coverage
payer_agg = payer_rqd[['PATIENT', 'PAYER_COVERAGE', 'TOTAL_CLAIM_COST']]
payer_agg = payer_agg.groupby(['PATIENT'], as_index=False).agg({
"PAYER_COVERAGE":
"sum",
"TOTAL_CLAIM_COST":
"sum"
})
payer_agg['COVERAGE_PCT'] = (payer_agg['PAYER_COVERAGE']) / (
payer_agg['TOTAL_CLAIM_COST'])
bins_p = [-10, 0.00001, 0.5, 0.80, 11]
labels_p = ['NO COVERAGE', 'LOW', 'MED', 'HIGH']
payer_agg["PAYERBUCKET"] = pd.cut(payer_agg['COVERAGE_PCT'],
bins=bins_p,
labels=labels_p)
payer_agg = pd.get_dummies(data=payer_agg, columns=['PAYERBUCKET'])
payer_agg = payer_agg.drop(['PAYER_COVERAGE', 'TOTAL_CLAIM_COST'], axis=1)
payer_agg = payer_agg.drop_duplicates()
#--- Caculate latest payer
payer_name = pd.merge(payer_t, payer_df1, left_on='PAYER', right_on='Id')
payer_year = payer_name.groupby(['PATIENT'],
as_index=False).agg({"END_YEAR": "max"})
payer_name2 = pd.merge(payer_name,
payer_year,
left_on=['PATIENT', 'END_YEAR'],
right_on=['PATIENT', 'END_YEAR'])
payer_name2 = payer_name2.drop(
def cal_ic(index, begt, endt, cycle, kind, **kargs):
chosen_ = kargs.get('chosen', [])
sector = kargs.get('sector', '')
mode = kargs.get('mode', 'all')
#date_list = [parse(str(x)).strftime('%Y-%m-%d') for x in get_ts_td(begt,endt,cycle)]
date_list = [
parse(str(x)).strftime('%Y-%m-%d') for x in get_tc_td(begt, endt)
]
#import pdb;pdb.set_trace()
ic_dict = {}
if len(sector):
sector_stks_dict = load_zzhy_all(DATA_PATH)
small_comb_num = 5
big_comb_num = 6
for i in range(small_comb_num, big_comb_num):
if not len(chosen_):
chosen = [';'.join(x) for x in list(combinations(kind, i))]
else:
chosen = chosen_
for factors in list(combinations(kind, i)):
bbb = ';'.join(factors)
if bbb.split(';') not in [x.split(';') for x in chosen]:
continue
for f in factors:
if i > 1:
ic_dict[';'.join(factors)] = [''] * (len(date_list) - 1)
else:
ic_dict[f] = [''] * (len(date_list) - 1)
for x in range(0, len(date_list) - 1):
INTdate = int(date_list[x].replace('-', ''))
print(INTdate)
'''
if not len(sector):
factor = get_fin(INTdate)
else:
factor = get_fin(INTdate,sector=sector)
'''
factor = get_fin(INTdate,
sector=sector,
sector_stks_dict=sector_stks_dict)
factor = factor.set_index('代码')
#import pdb;pdb.set_trace()
#factor = score_factors(factor,VALUE+PROFIT+TRADE+GROWTH_,GROWTH)
factor = score_factors(factor, VALUE + PROFIT + GROWTH_, GROWTH)
#import pdb;pdb.set_trace()
for i in range(small_comb_num, big_comb_num):
if not len(chosen_):
chosen = [';'.join(x) for x in list(combinations(kind, i))]
else:
chosen = chosen_
for factors in list(combinations(kind, i)):
bbb = ';'.join(factors)
if bbb.split(';') not in [x.split(';') for x in chosen]:
continue
score = []
for f in factors:
f_score = factor[[f]]
if not len(score):
score = f_score
else:
#import pdb;pdb.set_trace()
score = score.join(f_score)
score = score.mean(axis=1)
score = score.dropna()
score.name = 'f'
ret = factor[['下一个月涨幅']]
ret = ret.join(score)
ret = ret.dropna()
#import pdb;pdb.set_trace()
fut_ret_rank = pd.cut(ret['下一个月涨幅'], 10, labels=False) + 1
factor_rank = pd.cut(ret['f'], 10, labels=False) + 1
ic = stats.spearmanr(fut_ret_rank, factor_rank)[0]
#import pdb;pdb.set_trace()
if i > 1:
ic_dict[';'.join(factors)][x] = ic
else:
#import pdb;pdb.set_trace()
ic_dict[f][x] = ic
#import pdb;pdb.set_trace()
ic_df = pd.DataFrame(ic_dict)
t_stat, p_value = stats.ttest_1samp(ic_df, 0)
#import pdb;pdb.set_trace()
sorted_columns = [
'1/PFCF', 'EBITDA/EV', 'S/EV', '1/PE', '1/PB', '1/PS', '1/POP',
'CROIC', 'ROIC', 'ROIC1', 'EP', 'EBITDA-资本支出/IC', 'ROE', 'ROE(扣除)',
'ROA', 'FCF/营业收入', '毛利/净资产', '毛利率', '净利率', '销售现金比', '现金营业收入比',
'delta毛利率', 'deltaROE', '负债/投入资本', '外部融资额/总资产', '资产负债率', '有形净值债务率',
'经营现金净流入/资本支出', '折旧/投入资本', '长期负债变化率', '近两年平均资本支出增长率', 'delta存货周转率',
'delta应收账款周转率', 'TTMFCF增长率', 'TTM净利润增长率', 'TTM营业利润增长率', 'TTM营业收入增长率',
'最新一季净利润增长率', '最新一季营业利润增长率', '最新一季营业收入增长率', '近一个月跌幅', 'SKEW', '5/60',
'换手率', 'ILLIQ', '波动率', '目标收益率', '预期增长率', '预期PE', '预期PEG', 'Delta预期增长率',
'Delta预期增长率2', 'Delta预期当年净利润增长率2', '总市值'
]
#import pdb;pdb.set_trace()
IC = pd.DataFrame(ic_df.mean(), columns=['IC'])
IC['STD'] = ic_df.std()
IC['IR'] = IC['IC'] / ic_df.std()
#import pdb;pdb.set_trace()
IC['T'] = t_stat
IC['P'] = p_value
if not len(mode):
alpha_factor = IC.sort_values(by='T', ascending=False)
selected = alpha_factor
selected.to_csv('IC_{0}_{1}_{2}_{3}_{4}_{5}_{6}.csv'.format(
begt, endt, index, 'AllbyT', str(small_comb_num),
str(big_comb_num), sector))
else:
IC = IC.reindex_axis(kind)
selected = IC
selected.to_csv('IC_{0}_{1}_{2}_{3}_{4}_{5}_{6}.csv'.format(
begt, endt, index, 'all', str(small_comb_num), str(big_comb_num),
sector))
return selected
Fraudcheck.head()
Fraudcheck.columns
Le = preprocessing.LabelEncoder()
Fraudcheck['undergrad'] = Le.fit_transform(Fraudcheck['Undergrad'])
Fraudcheck['marital_Status'] = Le.fit_transform(Fraudcheck['Marital_Status'])
Fraudcheck['urban'] = Le.fit_transform(Fraudcheck['Urban'])
#Droping
Fraudcheck.drop(["Undergrad"], inplace=True, axis=1)
Fraudcheck.drop(["Marital_Status"], inplace=True, axis=1)
Fraudcheck.drop(["Urban"], inplace=True, axis=1)
# converting float value to integer S we are converting float to catogorical data
bins = [-1, 30000, 100000]
Fraudcheck["Taxable_Income"] = pd.cut(Fraudcheck["Taxable_Income"],
bins,
labels=["Risky", "Good"])
colnames = list(Fraudcheck.columns)
predictors = colnames[1:6] #inputs
target = colnames[0] #outputs
X = Fraudcheck[predictors]
Y = Fraudcheck[target]
####### GridSearch
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(n_jobs=2,
oob_score=True,
n_estimators=1000,
criterion="entropy")
print('-' * 60)
# 四、RMF分析实现过程
# 4.1数据转换
recency_value = sales_data['ORDERDATE'].groupby(
sales_data.index).max() # 计算原始最近一次订单时间
frequency_value = sales_data['ORDERDATE'].groupby(
sales_data.index).count() # 计算原始订单频率
monetary_value = sales_data['AMOUNTINFO'].groupby(
sales_data.index).sum() # 计算原始订单总金额
# 4.2计算RFM得分
# 分别计算R、F、M得分
deadline_date = pd.datetime(2017, 1, 1) # 指定一个时间节点，用于计算其他时间与该时间的距离
r_interval = (deadline_date - recency_value).dt.days # 计算R间隔
r_score = pd.cut(r_interval, 5, labels=[5, 4, 3, 2, 1]) # 计算R得分
f_score = pd.cut(frequency_value, 5, labels=[1, 2, 3, 4, 5]) # 计算F得分
m_score = pd.cut(monetary_value, 5, labels=[1, 2, 3, 4, 5]) # 计算M得分
# R、F、M数据合并
rfm_list = [r_score, f_score, m_score] # 将r、f、m三个维度组成列表
rfm_cols = ['r_score', 'f_score', 'm_score'] # 设置r、f、m三个维度列名
rfm_pd = pd.DataFrame(np.array(rfm_list).transpose(),
dtype=np.int32,
columns=rfm_cols,
index=frequency_value.index) # 建立r、f、m数据框
print('RFM Score Overview:')
print(rfm_pd.head(4))
print('-' * 60)
# 计算RFM总得分:加权得分
def calc_age_bins(self):
self.Xy['age_bin'] = pd.cut(self.Xy.age, bins=[0,10,20,30, 40, 50, 60, np.inf])
1: 'Married',
2: 'Widowed',
3: 'Divorced',
4: 'Separated',
5: 'Never married',
6: 'Living with partner',
77: 'Refused',
99: 'dont know'
})
da['DMDMARTL2'].value_counts()
da["RIAGENDRx"] = da.RIAGENDR.replace({1: "Male", 2: "Female"})
a = da.groupby(da["RIAGENDRx"])['DMDMARTL2'].value_counts()
da["agegrp"] = pd.cut(da.RIDAGEYR, [30, 40])
b = da.groupby([da["RIAGENDRx"], da['agegrp']])['DMDMARTL2'].value_counts()
b.loc['Male', :].unstack()
# __Q1a.__ Briefly comment on some of the differences that you observe between the distribution of marital status between women and men, for people of all ages.
# __Q1b.__ Briefly comment on the differences that you observe between the distribution of marital status states for women between the overall population, and for women between the ages of 30 and 40.
# __Q1c.__ Repeat part b for the men.
# ## Question 2
#
# Restricting to the female population, stratify the subjects into age bands no wider than ten years, and construct the distribution of marital status within each age band. Within each age band, present the distribution in terms of proportions that must sum to 1.
def Ks2(data, flag):
# Bin que vai permitir agrupar os valores por score
bin = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
#Contar o total de ocorrências de bons e mals pagadores
dfTotal = data.ALVO.value_counts().to_frame()
dfTotal = dfTotal.rename(index={0: "Bom", 1: "Mal"})
totais = dfTotal.T #totais.at['ALVO','Bom'] e totais.at['ALVO','Mal']
#Contando por percentual de decil
df = data.groupby(pd.cut(data.SCORE, bins=bin))['ALVO'].value_counts()
# Desempilhando o Dataframe
df = df.unstack()
# Calculando o percentual do decil comparado ao valor total
df["PercentualBom"] = (df[0] / totais.at['ALVO', 'Bom'])
df["PercentualMal"] = (df[1] / totais.at['ALVO', 'Mal'])
# Calculando o Cumulativo
df["PercentualBomAcc"] = df['PercentualBom'].rolling(min_periods=1,
window=10).sum()
df["PercentualMalAcc"] = df['PercentualMal'].rolling(min_periods=1,
window=10).sum()
# Calculando o ks2 por decil
df['KS2'] = abs(df["PercentualBomAcc"] - df["PercentualMalAcc"])
# Label
label = [
"0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80",
"80-90", "90-100"
]
flag = flag.strip().lower()
if flag == "no":
# Texto da Tabela descrevendo gráfico
desc = """O KS2 é uma métrica utilizada para sabermos quanto o modelo discrimina os bons dos maus clientes.
Seu valor é a maior diferença das distribuições acumuladas dos dois públicos analisados.
Quanto maior o KS2, melhor será a discriminação dos dois públicos pelo modelo em análise."""
#fig = go.Figure()
# Fazendo os subplots para colocar a descrição e o gráfico na mesma imagem
fig = make_subplots(rows=1,
cols=3,
specs=[[{
"type": "table"
}, {
"colspan": 2
}, None]])
# Add Table
fig.add_trace(go.Table(header=dict(values=["Descrição"],
font=dict(size=10),
align="left"),
cells=dict(values=[desc], align="left")),
row=1,
col=1)
# Add linha de Bom
fig.add_trace(
go.Scatter(x=label,
y=df.PercentualBomAcc,
hovertemplate="%{x},%{y}",
name="Bom"))
# Add linha de Mal
fig.add_trace(go.Scatter(x=label,
y=df.PercentualMalAcc,
hovertemplate="%{x},%{y}",
name="Mal"),
row=1,
col=2)
# Add linha de KS2
fig.add_trace(go.Scatter(x=label, y=df.KS2, name="KS2"), row=1, col=2)
fig.update_xaxes(title_text="Faixa de Score", row=1, col=2)
fig.update_yaxes(title_text="% População (AC)", row=1, col=2)
fig.update_yaxes(tickformat=".3%", row=1, col=2)
#Add Formatação do Gráfico
fig.update_layout(title={
'text': "KS2",
'y': 0.9,
'x': 0.5,
'xanchor': 'center',
'yanchor': 'top'
}, )
fig.show()
return fig
elif flag == "yes":
pre_fig = make_subplots(rows=1, cols=2, specs=[[{"colspan": 2}, None]])
# Add linha de Bom
pre_fig.add_trace(
go.Scatter(x=label,
y=df.PercentualBomAcc,
hovertemplate="%{x},%{y}",
name="Bom"))
# Add linha de Mal
pre_fig.add_trace(go.Scatter(x=label,
y=df.PercentualMalAcc,
hovertemplate="%{x},%{y}",
name="Mal"),
row=1,
col=1)
# Add linha de KS2
pre_fig.add_trace(go.Scatter(x=label, y=df.KS2, name="KS2"),
row=1,
col=1)
pre_fig.update_xaxes(title_text="Faixa de Score", row=1, col=1)
pre_fig.update_yaxes(title_text="% População (AC)", row=1, col=1)
pre_fig.update_yaxes(tickformat=".3%", row=1, col=1)
#Add Formatação do Gráfico
pre_fig.update_layout(title={
'text': "KS2",
'y': 0.9,
'x': 0.5,
'xanchor': 'center',
'yanchor': 'top'
}, )
pre_fig.show()
desc = str(input("digite a descrição desejada: "))
# Fazendo os subplots para colocar a descrição e o gráfico na mesma imagem
fig = make_subplots(rows=1,
cols=3,
specs=[[{
"type": "table"
}, {
"colspan": 2
}, None]])
# Add Table
fig.add_trace(go.Table(header=dict(values=["Descrição"],
font=dict(size=10),
align="left"),
cells=dict(values=[desc], align="left")),
row=1,
col=1)
# Add linha de Bom
fig.add_trace(
go.Scatter(x=label,
y=df.PercentualBomAcc,
hovertemplate="%{x},%{y}",
name="Bom"))
# Add linha de Mal
fig.add_trace(go.Scatter(x=label,
y=df.PercentualMalAcc,
hovertemplate="%{x},%{y}",
name="Mal"),
row=1,
col=2)
# Add linha de KS2
fig.add_trace(go.Scatter(x=label, y=df.KS2, name="KS2"), row=1, col=2)
fig.update_xaxes(title_text="Faixa de Score", row=1, col=2)
fig.update_yaxes(title_text="% População (AC)", row=1, col=2)
fig.update_yaxes(tickformat=".3%", row=1, col=2)
#Add Formatação do Gráfico
fig.update_layout(title={
'text': "KS2",
'y': 0.9,
'x': 0.5,
'xanchor': 'center',
'yanchor': 'top'
}, )
fig.show()
return fig
else:
raise " Flag invalida, por favor digite 'YES' ou 'NO' "
print(TopFin_df)
#Bottom Five Schools
BotFin_df = Place_df.tail(5)
print(BotFin_df)
#Math Scores by Grade
MBG_df = Stud_df.groupby(["school_name", "grade"])[["math_score"]].mean()
print(round(MBG_df, 2))
RBG_df = Stud_df.groupby(["school_name", "grade"])[["reading_score"]].mean()
print(round(RBG_df, 2))
#Cutting the dataframe into intervals: 575-599, 600 to 629, 630 to 649, 650+
Fin_df["Spending Ranges"] = pd.cut(
Fin_df["Per Student Budget"], [575, 599, 629, 649, 674],
labels=["Below $600", "$600-$629", "$630-$649", "At Least $650"])
#scores by school spending
BySpend_df = Fin_df.groupby(["Spending Ranges"])[[
"Avg Math Score", "Avg Reading Score", "% Passing Math",
"% Passing Reading", "Overall Passing Rate"
]]
print(round(BySpend_df.mean(), 2))
#Cutting the data into 3 groups by School Size
Fin_df["Size Ranges"] = pd.cut(Fin_df["Total Students"], [0, 1499, 2999, 5000],
labels=["Small", "Medium", "Large"])
BySize_df = Fin_df.groupby(["Size Ranges"])[[
"Avg Math Score", "Avg Reading Score", "% Passing Math",
"% Passing Reading", "Overall Passing Rate"
def create_ml_100k_dataset(embed_dim=16):
"""加载数据"""
base_path = '/home/mesie/python/recommend/recommend-learning/data/'
rating_df = pd.read_csv(
base_path + 'ml-100k/u.data',
sep='\t',
names=['user_id', 'movie_id', 'rating', 'timestamp'])
user_df = pd.read_csv(
base_path + 'ml-100k/u.user',
sep='|',
names=['user_id', 'age', 'gender', 'occupation', 'zip'])
item_df = pd.read_csv(base_path + 'ml-100k/u.item',
sep='|',
encoding="ISO-8859-1",
names=[
'movie_id', 'title', 'release_date',
'video_release_date', 'url', 'unknown', 'Action',
'Adventure', 'Animation', 'Children', 'Comedy',
'Crime', 'Documentary', 'Drama', 'Fantasy',
'Film-Noir', 'Horror', 'Musical', 'Mystery',
'Romance', 'Sci-Fi', 'Thriller', 'War', 'Western'
])
data_df = pd.merge(rating_df, user_df, how="left")
data_df = pd.merge(data_df, item_df, how="left")
data_df['label'] = data_df['rating'].apply(get_label)
# 处理age
data_df['age'] = pd.cut(
data_df['age'],
bins=[0, 15, 25, 35, 45, 60, 100],
labels=['0-15', '15-25', '25-35', '35-45', '45-60', '60-100'])
# 处理电影类型
user_features = ['user_id', 'age', 'gender']
movie_features = [
'movie_id', 'unknown', 'Action', 'Adventure', 'Animation', 'Children',
'Comedy', 'Crime', 'Documentary', 'Drama', 'Fantasy', 'Film-Noir',
'Horror', 'Musical', 'Mystery', 'Romance', 'Sci-Fi', 'Thriller', 'War',
'Western'
]
# features = user_features + movie_features
feature_max_idx = {}
for feature in user_features + movie_features:
lbe = LabelEncoder()
data_df[feature] = lbe.fit_transform(data_df[feature])
feature_max_idx[feature] = data_df[feature].max() + 1
user_feat_cols = []
user_feat_cols.append(
sparseFeature(feat='user_id',
feat_num=feature_max_idx['user_id'],
embed_dim=embed_dim))
user_feat_cols = user_feat_cols + [
sparseFeature(feat=uf, feat_num=feature_max_idx[uf])
for uf in ['age', 'gender']
]
item_feat_cols = []
item_feat_cols.append(
sparseFeature(feat='movie_id',
feat_num=feature_max_idx['movie_id'],
embed_dim=embed_dim))
movie_type = [
'unknown', 'Action', 'Adventure', 'Animation', 'Children', 'Comedy',
'Crime', 'Documentary', 'Drama', 'Fantasy', 'Film-Noir', 'Horror',
'Musical', 'Mystery', 'Romance', 'Sci-Fi', 'Thriller', 'War', 'Western'
]
item_feat_cols = item_feat_cols + [
sparseFeature(feat=mt, feat_num=feature_max_idx[mt])
for mt in movie_type
]
train, test = train_test_split(data_df, test_size=0.2)
train_X = [{feat: train[feat].values
for feat in user_features},
{feat: train[feat].values
for feat in movie_features}]
# train_X = [train[user_features].values.astype('int32'), train[movie_features].values.astype('int32'),
# train['label'].values.astype('int32')]
train_y = train['label'].values.astype('int32')
test_X = [{feat: test[feat].values
for feat in user_features},
{feat: test[feat].values
for feat in movie_features}]
# test_X = [test[user_features].values.astype('int32'), test[movie_features].values.astype('int32'),
# test['label'].values.astype('int32')]
test_y = test['label'].values.astype('int32')
print(train_X)
return user_feat_cols, item_feat_cols, train_X, train_y, test_X, test_y
def calc_age_bins(self):
"""Calculates age bins.
"""
self.Xy["age_bin"] = pd.cut(self.Xy.age, bins=self.age_bins)
#from sklearn.svm import SVC
#from sklearn.svm import LinearSVC
from sklearn.decomposition import PCA
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix
print('Loading dataframe...')
t0 = time()
with open('jokes.df.pickle', 'rb') as pickle_file:
df = pickle.load(pickle_file)
print("done in %0.3fs" % (time() - t0))
print('Flattening {} d2v lists ...'.format(df.d2v.values.shape[0]))
X = np.reshape([r for r in df.d2v.values], (df.d2v.values.shape[0], 300))
y = pd.cut(df.score, [-1, 15, 50000], labels=['MEH', 'GOOD'])
print(df.score.describe())
print("done in %0.3fs" % (time() - t0))
print('Splitting into a training and testing set ...')
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.25,
random_state=42)
print("done in %0.3fs" % (time() - t0))
print("Projecting the input data on the d2v orthonormal basis (PCA'ing) ...")
pca = PCA(n_components=32, svd_solver='randomized', whiten=True).fit(X_train)
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done in %0.3fs" % (time() - t0))
data['title'] = data['title'].fillna(0)
data['title'] = data['title'].astype(int)
data['title']
pd.value_counts(data['title'])
# age - standardscaler
data['age'].value_counts()
data['age'].describe()
data['age'].fillna(method='pad', inplace=True)
np.bincount(data['age'].isnull())
# age_range - encoding
bins = [0, 5, 10, 15, 20, 30, 40, 50, 60, 80, 100]
labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
data['age_range'] = pd.cut(data['age'], bins, labels=labels)
data['age_range']
np.bincount(data['age_range'].isnull())
data.age_range.unique()
sns.countplot(data['age_range'], data=data, hue=data['survived'])
plt.close('all')
# del data['age']
data['age_range']
# cabin-> cabin list -> encoding
data['cabin'].unique()
np.bincount(data['cabin'].isnull())
data['cabin']
data.loc[data.cabin.str[0] == 'A', 'cabinlist'] = 1
data.loc[data.cabin.str[0] == 'B', 'cabinlist'] = 2
parser = argparse.ArgumentParser()
parser.add_argument("input", help="model TSV")
parser.add_argument("faceted_output", help="faceted model fold change scatterplot (PDF, PNG, etc.)")
parser.add_argument("combined_output", help="single-panel model fold change scatterplot (PDF, PNG, etc.)")
parser.add_argument("year_range")
parser.add_argument("viruses")
parser.add_argument("predictors")
parser.add_argument("sample")
args = parser.parse_args()
frequency_bins = np.linspace(0.0, 1.0, 5)
df = pd.read_table(args.input)
df["observed_ratio"] = df["observed_freq"] / df["initial_freq"]
df["predicted_ratio"] = df["predicted_freq"] / df["initial_freq"]
df["binned_initial_freq"] = pd.cut(df["initial_freq"], bins=frequency_bins)
correlation_by_bin = []
mcc_by_bin = []
n_clades_by_bin = []
for freq, freq_df in df.groupby("binned_initial_freq"):
correlation_by_bin.append(pearsonr(freq_df["observed_ratio"], freq_df["predicted_ratio"])[0])
# Calculate Matthew's correlation coefficient
mcc_by_bin.append(get_matthews_correlation_coefficient_for_data_frame(freq_df))
n_clades_by_bin.append(freq_df.shape[0])
g = sns.FacetGrid(df, col="binned_initial_freq", col_wrap=2, height=4)
g.map(sns.regplot, "observed_ratio", "predicted_ratio", fit_reg=False)
g.add_legend()
[
train_data.Age[train_data.Pclass == i].plot.kde(bw_method=0.3,
color=colors[i - 1])
for i in [1, 2, 3]
]
plt.xlim((-20, 100))
plt.legend(["First class", "Second class", "Third class"])
plt.title("Density plot Age wrt Class of Travel")
# Consider Age categories instead of specific ages
train_data.Age = train_data.Age.fillna(-0.5)
labels = ['Unknown', 'Babies', 'Children', 'Youth', 'Adults', 'Seniors']
bins = [-1, 0, 5, 15, 24, 65, np.inf]
train_data['AgeCategories'] = pd.cut(train_data["Age"], bins, labels=labels)
survival_rate_per_category = (
train_data.AgeCategories[train_data.Survived == 1].value_counts(
normalize=True).sort_index() /
train_data.AgeCategories[train_data.Survived == 0].value_counts(
normalize=True).sort_index())
plt.subplot2grid((1, 3), (0, 2))
survival_rate_per_category.plot(
kind="bar",
alpha=0.6,
color=["lightblue", "teal", "wheat", "beige", "grey", "lavender"])
plt.ylabel("survival rate")
plt.title("Survival Rate per Age Category")
# In[4]:
df = pd.read_csv ('spotify_songs.csv')
# In[6]:
lyrics = df['lyrics'].values.astype(str)
# In[41]:
y = pd.cut(df.valence,bins=[0,0.5,1],labels=[0,1])
# In[42]:
lyrics_train, lyrics_test, y_train, y_test = train_test_split(lyrics, y, random_state=1)
# In[44]:
vectorizer = CountVectorizer()
vectorizer.fit(lyrics_train)
import pandas as pd import numpy as np x = np.array([1, 7, 5, 4, 6, 3]) print(pd.cut(x, 3))
for d in data:
for i in range(0, 2):
for j in range(0, 3):
guess = d[(d['Sex'] == i) & (d['Pclass'] == j + 1)]['Age'].dropna()
age_guess = guess.mean()
ages[i, j] = int(age_guess / 0.5 + 0.5) * 0.5
for i in range(0, 2):
for j in range(0, 3):
d.loc[(d.Age.isnull()) & (d.Sex == i) & (d.Pclass == j + 1),
'Age'] = ages[i, j]
d['Age'] = d['Age'].astype(int)
print(train_data.head())
# Examine the survival rate among Age bands
train_data['AgeBand'] = pd.cut(train_data['Age'], 5)
print(train_data[['AgeBand', 'Survived']].groupby(
['AgeBand'], as_index=False).mean().sort_values(by='AgeBand',
ascending=True))
# Replace Age with AgeBand
for d in data:
d.loc[d['Age'] <= 16, 'Age'] = 0
d.loc[(d['Age'] > 16) & (d['Age'] <= 32), 'Age'] = 1
d.loc[(d['Age'] > 32) & (d['Age'] <= 48), 'Age'] = 2
d.loc[(d['Age'] > 48) & (d['Age'] <= 64), 'Age'] = 3
d.loc[d['Age'] > 64, 'Age']
print(train_data.head())
# Remove AgeBand feature
train_data = train_data.drop(['AgeBand'], axis=1)
fig4 = px.box(gss_clean,
x='job_prestige',
y='sex',
color='sex',
labels={
'job_prestige': 'Occupational Prestige',
'sex': ''
})
fig4.update_layout(showlegend=False)
fig4.update(layout=dict(title=dict(x=0.5)))
fig4.show()
new_df = gss_clean[['income', 'sex', 'job_prestige']]
new_df['job_prestige_cat'] = pd.cut(new_df['job_prestige'],
bins=6,
labels=['1', '2', '3', '4', '5', '6'])
new_df = new_df.sort_values(by='job_prestige_cat')
clean_df = new_df.dropna(axis=0)
fig_bar = px.box(clean_df,
x='sex',
y='income',
color='sex',
facet_col='job_prestige_cat',
facet_col_wrap=2,
hover_data=['income'],
labels={
'sex': 'Sex',
'income': 'Income'
# load data
data_temp = data_total[data_total['channel'] == str(c + 1)]
#print(data_temp)
# prepare binning (bin_list)
photon_max = max(data_temp['intensity [photon]'])
print('photon_max: {}'.format(photon_max))
binsize = 1000
photon_bin_max = photon_max // binsize
print('photon_bin_max: {}'.format(photon_bin_max))
bin_list = list(range(0, (int(photon_bin_max) + 2) * binsize, binsize))
print(bin_list)
# prepare binned data
data_total_tmp = data_total
data_total_tmp['bins'] = pd.cut(data_total['intensity [photon]'],
bins=bin_list)
# 1st group by bins
data_total_tmp = data_total_tmp.groupby(
by=['channel', 'group', 'filename', 'bins']).size()
# reset index
data_total_tmp = data_total_tmp.reset_index()
data_total_tmp = data_total_tmp.rename(index=int, columns={0: 'counts'})
# 2nd group by
data_total_tmp_mean = data_total_tmp.groupby(
by=['channel', 'group', 'bins']).mean()['counts']
data_total_tmp_sem = data_total_tmp.groupby(
by=['channel', 'group', 'bins']).sem()['counts']
print('binned data, mean')
display(data_total_tmp_mean)
print('binned data, sem')
# it will not cause much interference to the model for ages 200 # 3. LR is a generalized linear model with limited expressive power. # After discretization, each variable has a separate weight, # which is equivalent to introducing non-linearity, # which can improve the expressive power of the model and increase the fitting. # 4. After discrete features, feature crossover can be performed to improve the expression ability. # M + N variables are programmed by M + N variables, # which further introduces non-linearity and improves the expression ability； # 5. The model is more stable after the features are discrete. # For example, the user's age range will not change because the user is one year old. # LightGBM improve the performance of XGBoost and increase data hiving at the same time # which improves the generalization of the model bin = [i*10 for i in range(31)] data['power_bin'] = pd.cut(data['power'], bin, labels=False) print(data[['power_bin', 'power']].head()) #see the original data print(data.shape) print(data.columns) data = data.drop(['creatDate', 'regDate', 'regionCode'], axis=1) #check whether they have been deleted print(data.shape) print(data.columns) from sklearn import preprocessing min_max_scaler = preprocessing.MinMaxScaler() data['power'] = np.log(data['power'] + 1) data['power'] = ((data['power'] - np.min(data['power'])) / (np.max(data['power']) - np.min(data['power']))) data['kilometer'] = ((data['kilometer'] - np.min(data['kilometer'])) /
# In[93]:
df.plot(kind='line', x=df.Genres, figsize=[9, 3])
plt.title('What movies watch writers?')
plt.ylabel('count')
plt.xlabel('genres')
##### 10. Which age group is ranking which genre the most?
# In[101]:
# we are dividing people in 8 group by age.
labels = ['0-9', '10-19', '20-29', '30-39', '40-49', '50-59', '60-69', '70-79']
# add new column with age_group
data['Age_group'] = pd.cut(data['Age'],
range(0, 81, 10),
right=False,
labels=labels)
print data[['Age', 'Age_group']].drop_duplicates()[:20]
# In[95]:
# let's look at how age is distributed amongst our users
# call panda's "hist" on the column to produce a histogram
data.Age.hist(bins=10)
plt.title("Distribution of users' ages")
plt.ylabel('count of users')
plt.xlabel('age')
# In[96]:
# now we are comparing ratings across age groups
# alternative: here is the numpy equivalent to R ifelse
# targets_long['value'] = np.where(condition, targets_long['value'] * 1000, targets_long['value'])
# %% get advanced puf file
%time puf_2018 = pd.read_hdf(PUF_HDF) # 1 sec
puf_2018.tail()
puf_2018.columns.sort_values().tolist() # show all column names
pufsub = puf_2018.copy() # new data frame
pufsub = pufsub.loc[pufsub["data_source"] == 1] # ~7k records dropped
# create an irs stub categorical variable
pufsub['IRS_STUB'] = pd.cut(
pufsub['c00100'],
IRS_AGI_STUBS,
labels=list(range(1, len(IRS_AGI_STUBS))),
right=False)
pufsub.columns.sort_values().tolist() # show all column names
# %% get just the variables we want and create new needed variables
# get list of target variables we need to have
plist = list(PUFTARG_XWALK.keys())
plist
# add names of variables we need for calculations or targeting
plist.append('pid')
plist.append('IRS_STUB')
plist.append('s006')
plist.append('MARS')
# remove names of variables we are going to create
def main():
inpt_folder = "/mnt/scratch/Data/METData/"
v_frac = 0.1
## Create a dummy model as it has all of the loader capabilities and ensure this is exactly what our networks see during training!
model = Model.METNET_Agent('Tight',
'/mnt/scratch/Saved_Networks/Presentation/')
model.setup_network(True, 'XXX', None, 1, 5, False, 0, dev='cpu')
model.inpt_list = ['Tight_Final_ET']
model.setup_dataset(inpt_folder,
v_frac,
32,
1024,
8096,
8,
'mag',
0,
0,
0,
no_trn=True)
## The bin setup to use for the profiles
n_bins = 40
mag_bins = np.linspace(0, 400, n_bins + 1)
exy_bins = [
np.linspace(-50, 250, n_bins + 1),
np.linspace(-150, 150, n_bins + 1)
]
## All the networks outputs and targets for the batch will be combined into one list
all_outputs = []
all_targets = []
## The information to be saved in our dataframe, the truth et (for binning) and the performance metric per bin
met_names = ['Tru', 'Res', 'Lin', 'Ang']
## Configure pytorch, the network and the loader appropriately
T.set_grad_enabled(False)
model.net.eval()
model.valid_loader.dataset.weight_off()
## Iterate through the validation set
for batch in tqdm(model.valid_loader, desc='perfm', ncols=80, ascii=True):
## Get the network outputs and targets
tight, targets = myUT.move_dev(batch[:-1], model.device)
## Undo the processing on Tight
tight = (tight * model.net.inp_stats[1, :1] +
model.net.inp_stats[0, :1]) / 1000
outputs = T.cat([tight, T.zeros_like(tight)], dim=1)
all_outputs.append(deepcopy(outputs))
all_targets.append(deepcopy(targets))
## Combine the lists into single tensors
all_outputs = T.cat(all_outputs)
all_targets = T.cat(all_targets)
## Undo the normalisation on the outputs and the targets
net_xy = all_outputs
tru_xy = (all_targets * model.net.trg_stats[1] +
model.net.trg_stats[0]) / 1000
net_et = T.norm(net_xy, dim=1)
tru_et = T.norm(tru_xy, dim=1)
## Calculate the performance metrics
res = ((net_xy - tru_xy)**2).mean(dim=1)
lin = (net_et - tru_et) / (tru_et + 1e-8)
ang = T.acos(
T.sum(net_xy * tru_xy, dim=1) /
(net_et * tru_et + 1e-8))**2 ## Calculated using the dot product
## We save the overall resolution
model.avg_res = T.sqrt(res.mean()).item()
## Combine the performance metrics into a single pandas dataframe
combined = T.vstack([tru_et, res, lin, ang]).T
df = pd.DataFrame(myUT.to_np(combined), columns=met_names)
## Make the profiles in bins of True ET using pandas cut and groupby methods
df['TruM'] = pd.cut(df['Tru'],
mag_bins,
labels=(mag_bins[1:] + mag_bins[:-1]) / 2)
profs = df.drop('Tru', axis=1).groupby('TruM', as_index=False).mean()
profs['Res'] = np.sqrt(profs['Res']) ## Res and Ang are RMSE measurements
profs['Ang'] = np.sqrt(profs['Ang'])
## Save the performance profiles
profs.to_csv(Path(model.save_dir, model.name, 'perf.csv'), index=False)
## Save the Magnitude histograms
h_tru_et = np.histogram(myUT.to_np(tru_et), mag_bins, density=True)[0]
h_net_et = np.histogram(myUT.to_np(net_et), mag_bins, density=True)[0]
myPL.plot_and_save_hists(Path(model.save_dir, model.name, 'MagDist'),
[h_tru_et, h_net_et], ['Truth', 'Outputs'],
['MET Magnitude [Gev]', 'Normalised'],
mag_bins,
do_csv=True)
## Save the ex and ey contour plots
h_tru_xy = np.histogram2d(*myUT.to_np(tru_xy).T, exy_bins, density=True)[0]
h_net_xy = np.histogram2d(*myUT.to_np(net_xy).T, exy_bins, density=True)[0]
myPL.plot_and_save_contours(Path(model.save_dir, model.name, 'ExyDist'),
[h_tru_xy, h_net_xy], ['Truth', 'Outputs'],
['METx [GeV]', 'METy [GeV]'],
exy_bins,
do_csv=True)
## Get a dataframe from the class dict and write out
dict_df = pd.DataFrame.from_dict([model.get_dict()]).set_index('name')
dict_df.to_csv(Path(model.save_dir, model.name, 'dict.csv'))
def loadAndTransDatasetsReal(self):
"""
读入样本数据(随机生成的模拟真实数据)
给连续特征分组, 返回修改后的新样本数据
同时可输出特征分组文件, 供手动修改分组权重值
"""
df = self.df_train.copy()
# 1 特征转换: 指标值分组和转换
# 1.1 对类别特征, 用[特征名+'__'+特征值]的结果替换原来的特征值
# 如将'建筑业'替换成'industry__建筑业'
for f1 in [
'industry', 'city', 'district', 'is_change_premises',
'cacellation_reason', 'is_real_premise', 'is_executee',
'is_financial_black_list', 'alert_level'
]:
df[f1] = df[f1].map(lambda x: f1 + '__' + str(x))
#print(df[f1].value_counts())
# 1.2 对连续特征值, 先分组, 再用[指标名+'__'+分组]的结果替换分组后的指标值
for f2 in [
'registered_capital',
'employees',
'total_assets',
'total_tax',
'industry_index',
'fraud_score',
'period_abnormal',
'rate_frozon_holdings',
'total_liabilities',
'total_debt',
'judical_auction_amount',
'complaints_number_monthly',
'rate_conciliation',
'sub_enterprises_number',
]:
df[f2] = pd.cut(
df[f2], 4, duplicates='drop').map(lambda x: f2 + '__' + str(x))
#print(df[f2].value_counts())
# 1.3 给个别指标手动指定分组后改名
#print(df['period_abnormal'].value_counts())
#print(pd.cut(df['period_abnormal'], bins=5))
del df['enterprise_id']
# 2 输出转换后的数据集
# 2.1 输出所有指标的所有分组值, 存储在features_group_name.xlsx文件中
df_features_group = pd.DataFrame()
for c in df.columns:
#print(c)
df1 = df[c].value_counts().reset_index()
df1.columns = ['feature_group_name', 'counts']
df1['interval'] = df1['feature_group_name'].map(
lambda x: x.split('__')[1])
#print(df1[['features', 'interval', 'counts']])
df_features_group = pd.concat([
df_features_group,
df1[['feature_group_name', 'interval', 'counts']]
])
#print(df_features_group)
df_features_group.to_excel('datasets/features_group_name.xlsx',
index=False)
# 2.2 存储分组转换后的指标值
#print(df.shape, '\n', df.head(), '\n', df.columns)
df.to_csv('datasets/features_grouped.csv', sep=';', index=False)
# 3 将特征转换后的数据集转换成数组格式, 模型利用datsets做正式训练
self.datasets_init = df.to_numpy().tolist()
return self.datasets_init
def mono_bin(Y, X, n=max_bin):
df1 = pd.DataFrame({"X": X, "Y": Y})
justmiss = df1[['X', 'Y']][df1.X.isnull()]
notmiss = df1[['X', 'Y']][df1.X.notnull()]
r = 0
while np.abs(r) < 1:
try:
d1 = pd.DataFrame({
"X": notmiss.X,
"Y": notmiss.Y,
"Bucket": pd.qcut(notmiss.X, n)
})
d2 = d1.groupby('Bucket', as_index=True)
r, p = stats.spearmanr(d2.mean().X, d2.mean().Y)
n = n - 1
except Exception as e:
n = n - 1
if len(d2) == 1:
n = force_bin
bins = algos.quantile(notmiss.X, np.linspace(0, 1, n))
if len(np.unique(bins)) == 2:
bins = np.insert(bins, 0, 1)
bins[1] = bins[1] - (bins[1] / 2)
d1 = pd.DataFrame({
"X":
notmiss.X,
"Y":
notmiss.Y,
"Bucket":
pd.cut(notmiss.X, np.unique(bins), include_lowest=True)
})
d2 = d1.groupby('Bucket', as_index=True)
d3 = pd.DataFrame({}, index=[])
d3["MIN_VALUE"] = d2.min().X
d3["MAX_VALUE"] = d2.max().X
d3["COUNT"] = d2.count().Y
d3["EVENT"] = d2.sum().Y
d3["NONEVENT"] = d2.count().Y - d2.sum().Y
d3 = d3.reset_index(drop=True)
if len(justmiss.index) > 0:
d4 = pd.DataFrame({'MIN_VALUE': np.nan}, index=[0])
d4["MAX_VALUE"] = np.nan
d4["COUNT"] = justmiss.count().Y
d4["EVENT"] = justmiss.sum().Y
d4["NONEVENT"] = justmiss.count().Y - justmiss.sum().Y
d3 = d3.append(d4, ignore_index=True)
d3["EVENT_RATE"] = d3.EVENT / d3.COUNT
d3["NON_EVENT_RATE"] = d3.NONEVENT / d3.COUNT
d3["DIST_EVENT"] = d3.EVENT / d3.sum().EVENT
d3["DIST_NON_EVENT"] = d3.NONEVENT / d3.sum().NONEVENT
d3["WOE"] = np.log(d3.DIST_EVENT / d3.DIST_NON_EVENT)
d3["IV"] = (d3.DIST_EVENT - d3.DIST_NON_EVENT) * np.log(
d3.DIST_EVENT / d3.DIST_NON_EVENT)
d3["VAR_NAME"] = "VAR"
d3 = d3[[
'VAR_NAME', 'MIN_VALUE', 'MAX_VALUE', 'COUNT', 'EVENT',
'EVENT_RATE', 'NONEVENT', 'NON_EVENT_RATE', 'DIST_EVENT',
'DIST_NON_EVENT', 'WOE', 'IV'
]]
d3 = d3.replace([np.inf, -np.inf], 0)
d3.IV = d3.IV.sum()
return (d3)
fig, ax = plt.subplots()
ax = sns.distplot(df['boneage'], bins=10)
ax.set(xlabel='Boneage (months)', ylabel='Density',
title='Boneage distribution');
boneage_mean = df['boneage'].mean()#mean age
boneage_div = 2*df['boneage'].std()
print(boneage_mean," ",boneage_div)
df['boneage_zscore'] = df['boneage'].map(lambda x:
(x - boneage_mean) / boneage_div)
df.dropna(inplace=True)
df['gender'] = df['male'].map(lambda x: 1 if x else 0)
df['boneage_category'] = pd.cut(df['boneage'], 10)
df['gender']
df['boneage_category']
print(df)
raw_train_df, raw_valid_df = train_test_split(df, test_size=VALIDATION_FRACTION,
random_state=2018)
raw_train_df.shape[0]
raw_valid_df.shape[0]
raw_valid_df.head()
print(df.loc[df['exists']==False,:])
def main():
logging.basicConfig(format="%(funcName)s: %(message)s", level=logging.INFO)
output_dir = './german_credit_showcase/'
sensitive_microdata_path = output_dir + 'german_credit_data.tsv'
if not path.exists(output_dir):
mkdir(output_dir)
if not path.exists(sensitive_microdata_path):
attributes = []
codes = {}
logging.info('Retrieving data documentation...')
codes_file = request.urlopen(
'https://archive.ics.uci.edu/ml/machine-learning-databases/statlog/german/german.doc'
).readlines()
logging.info('Retrieved')
state = 'skip'
attribute = ''
code = ''
value = ''
for line in [x.decode('UTF-8').strip() for x in codes_file]:
if line == '':
state = 'skip'
elif state == 'skip':
if str.startswith(line, 'Att'):
state = 'attribute'
elif state == 'attribute':
attribute = line.strip()
attributes.append(attribute)
codes[attribute] = {}
state = 'values'
elif state == 'values':
split = line.index(':') if ':' in line else -1
if split != -1:
code = line[:split].strip()
value = line[split + 1:].strip().replace(':', '-')
codes[attribute][code] = value
else:
codes[attribute][code] += ' ' + line.replace(':', '-')
attributes.append('Credit rating')
codes['Credit rating'] = {'1': 'Good', '2': 'Bad'}
logging.info('Retrieving data file...')
df = pd.read_csv(
'https://archive.ics.uci.edu/ml/machine-learning-databases/statlog/german/german.data',
sep=' ',
index_col=False,
header=None,
names=attributes)
logging.info('Retrieved')
logging.info('Processing dataset...')
values, labels = binValuesAndLabels(df['Duration in month'].max(), 12)
df['Duration in month'] = pd.cut(df['Duration in month'],
bins=values,
labels=labels)
values, labels = binValuesAndLabels(df['Credit amount'].max(), 2500)
df['Credit amount'] = pd.cut(df['Credit amount'],
bins=values,
labels=labels)
values, labels = binValuesAndLabels(df['Age in years'].max(), 20)
df['Age in years'] = pd.cut(df['Age in years'],
bins=values,
labels=labels)
df = df.astype(str).replace(to_replace=r'^nan$', value='', regex=True)
for att in attributes:
df[att] = df[att].replace(codes[att])
del df['foreign worker']
del df['Property']
del df['Telephone']
del df['Other debtors / guarantors']
del df['Number of people being liable to provide maintenance for']
del df['Other installment plans']
del df['Savings account/bonds']
del df['Present employment since']
del df['Status of existing checking account']
df.to_csv(sensitive_microdata_path, sep='\t', index=False)
logging.info('Processed')
config = {
'parallel_jobs': 1,
'memory_limit_pct': 90,
'use_columns': [],
'record_limit': -1,
'reporting_length': 5,
'reporting_precision': 2,
'reporting_threshold': 2,
'seeded': True,
'sensitive_zeros': [],
'prefix': 'credit',
'output_dir': output_dir,
'sensitive_microdata_path': sensitive_microdata_path,
'sensitive_microdata_delimiter': '\t',
'report_title': 'German Credit Data Showcase',
}
json.dump(config,
open(path.join('.', config['prefix'] + '_config.json'), 'w'),
indent=1)
config['aggregate'] = True
config['generate'] = True
config['navigate'] = True
config['evaluate'] = True
config['reportable_aggregates_path'] = path.join(
config['output_dir'], config['prefix'] + '_reportable_aggregates.tsv')
config['synthetic_microdata_path'] = path.join(
config['output_dir'], config['prefix'] + '_synthetic_microdata.tsv')
config['sensitive_aggregates_path'] = path.join(
config['output_dir'], config['prefix'] + '_sensitive_aggregates.tsv')
runPipeline(config)
from sklearn.linear_model import LogisticRegression
from sklearn import preprocessing
from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score
from sklearn.metrics import roc_curve, auc
import imblearn
import xgboost as xgb # conda install py-xgboost
import lime
import lime.lime_tabular
import warnings
from lime import submodular_pick
import matplotlib.pyplot as plt
data = pd.read_csv(os.path.join('..', 'result', 'all_right_wrong_i.csv'))
data.drop(['change_1m', 'change_3m'], axis=1, inplace=True)
data = data.assign(
MRS_TX_3=pd.cut(data['MRS_TX_3'], [-1, 2, 7], labels=[0, 1]))
wrong_data = data[data.ctype == 0]
wrong_data.drop(['ctype'], axis=1, inplace=True)
id_wrong_data = wrong_data[['ICASE_ID', 'IDCASE_ID']]
y_wrong_data = wrong_data[['MRS_TX_3']]
x_wrong_data = wrong_data.drop(['ICASE_ID', 'IDCASE_ID', 'MRS_TX_3'], axis=1)
aucs = []
for i in range(1):
X_train, X_test, y_train, y_test = train_test_split(x_wrong_data,
y_wrong_data,
test_size=0.3,
random_state=i,
stratify=y_wrong_data)
# for train_index, test_index in KFold(n_splits=10).split(x_wrong_data):
# Create a new dataframe with unique columns for station names and station ids
df_unique = df.drop_duplicates(
subset=['from_station_id', 'from_station_name'])
df_unique = df_unique[['from_station_id', 'from_station_name']]
df_unique = df_unique.reset_index(drop=True)
# Create a json file if needed
# with open('./frontend/file.json', 'w') as f:
# json.dump(df_unique.to_json(r'./frontend/file.json',
# orient="records"), f)
# Dropping rows with empty values
df.dropna(inplace=True)
# Convert the tripduration column into float type
df['tripduration'] = df['tripduration'].replace({
',': ''
}, regex=True).astype(float)
# Get the current year
now = datetime.now()
# Add a new column, age to the dataframe
df['age'] = now.year - df['birthyear']
# Separate the ages into groups
df['age_group'] = pd.cut(df['age'], [0, 15, 30, 45, 200],
labels=['0-15', '16-30', '31-45', '46+'])
app.run(debug=True)
'RU': 11162,
'SA': 22865,
'SE': 54608,
'TH': 7274,
'TR': 9370,
'TW': 24827,
'UA': 3592,
'US': 65111,
'VN': 2740,
'ZA': 11300,
'CO': 6500
})
df.gdpCountry = pd.to_numeric(df.gdpCountry, errors='coerce')
df['gdpCountry'] = df['gdpCountry'].fillna(11335)
df['gdpCountry'] = pd.cut(df.gdpCountry,
bins=[0, 29960, 50000, 150000],
labels=[0, 1, 2])
#End Yev Gdp
device_map = {
'IPhone7': 0,
'IPhone7Plus': 0,
'IPhone8Plus': 0,
'IPhone6S': 0,
'IPhoneSE': 0,
'IPhone8': 0,
'IPhone6SPlus': 0,
'IPadAir1G': 0,
'IPhone5S': 0,
'IPadMini3G': 0,