def draw_combined_hist(df, countries, country_names, winsorize=False):
#make a combined histogram
plt.cla()
plt.figure(1, figsize=(5, 3))
TRIM = 0.05 if winsorize == True else 0.0
m = 0
#get the max value amongsts all series that we are going to plot
for c in countries:
x = df[c].dropna().values
x = mstats.winsorize(x, (0, TRIM))
if max(x) > m:
m = max(x)
#we have the max value, now plot each series, bins are decided based on max
i = 0
for c in countries:
x = df[c].dropna().values
x = mstats.winsorize(x, (0, TRIM))
bins = np.linspace(0, m, m)
plt.hist(x, bins, alpha=0.5, label=country_names[i])
i += 1
plt.legend(loc='upper right')
plt.title(
'Histogram for distribution of Starbucks stores\n across cities in a country'
)
name = 'hist.png' if winsorize == False else 'winsorized_hist.png'
fname = os.path.join(glob.OUTPUT_DIR_NAME, glob.EDA_DIR, 'more', name)
plt.savefig(fname)
def plots(df,
transformation="boxcox",
fig_size=(15, 8),
whis=1.5,
wins=(0, 0)):
features_to_plot = df.columns[df.dtypes != object]
positive_features = list()
for feature in features_to_plot:
if (df[feature] > 0).all():
positive_features.append(feature)
if transformation == "boxcox":
for feature in positive_features:
df[feature], _ = boxcox(winsorize(df[feature], wins))
plt.figure(figsize=fig_size)
plt.subplot(1, 2, 1)
sns.boxplot(df[feature], whis=whis)
plt.subplot(1, 2, 2)
sns.distplot(df[feature])
plt.show()
elif transformation == "log":
for feature in positive_features:
df[feature] = np.log(winsorize(df[feature], wins))
plt.figure(figsize=fig_size)
plt.subplot(1, 2, 1)
sns.boxplot(df[feature])
plt.subplot(1, 2, 2)
sns.distplot(df[feature])
plt.show()
else:
print("tranformation type should be one of these:\n*log\n*boxcox")
def get_index_list_pe_pb_date(code_list, date):
'''指定日期的指数PE_PB'''
ret_dict = {}
df_all = get_fundamentals(query(valuation), date) # 某日所有股票
for code in code_list:
stocks = get_idx_components(code, date)
df = df_all[df_all['code'].isin(stocks)] # 某个指数
if len(df) > 0:
# 整体法，市值加权
df = df[df.pb_ratio != 0] # 去除0
df = df[df.pe_ratio != 0] # 去除0
pe1 = sum(df.market_cap) / sum(df.market_cap / df.pe_ratio)
pb1 = sum(df.market_cap) / sum(df.market_cap / df.pb_ratio)
# 等权，亏损置零
pe2 = len(df) / sum(1 / df.pe_ratio[df.pe_ratio > 0])
pb2 = len(df) / sum(1 / df.pb_ratio[df.pb_ratio > 0])
# 中位数，无需预处理
pe3 = df.pe_ratio.median()
pb3 = df.pb_ratio.median()
# 算数平均，取分位数95%置信区间
pe4 = mean(mstats.winsorize(df.pe_ratio, limits=0.025))
pb4 = mean(mstats.winsorize(df.pb_ratio, limits=0.025))
ret_dict[code] = {
'pe1': round(pe1, 2),
'pb1': round(pb1, 2),
'pe2': round(pe2, 2),
'pb2': round(pb2, 2),
'pe3': round(pe3, 2),
'pb3': round(pb3, 2),
'pe4': round(pe4, 2),
'pb4': round(pb4, 2),
}
return ret_dict
def winsorize_quantTrans(self,lower = 0.10, upper = 0.10,ignore_zero = True):
'''Winsorizes quantity
PARAMETERS
lower: lower percentile in which to convert values. All values below this threshold will be converted to the
lower percentile value
upper: upper percentile in which to convert values. All values above this threshold will be converted to the
upper percentile value
ignore_zero: winsorize on non-zero values
RETURNS
converts ratings to winsorized values
'''
if ignore_zero:
nonzero_ind = np.nonzero(self.quantity)[0]
self.quantity[nonzero_ind] = mstats.winsorize(self.quantity[nonzero_ind], limits=[lower, upper])
self._quantity['Transformation'].append('Winsorized nonzeros based on limits {}, {}'.format(lower,
upper))
else:
self.quantity = np.array(mstats.winsorize(self.quantity,limits=[lower, upper]))
self._quantity['Transformation'].append('Winsorized based on limits {}, {}'.format(lower,upper))
def test_winsorization(self):
"Tests the Winsorization of the data."
data = ma.array([77, 87, 88, 114, 151, 210, 219, 246, 253, 262, 296, 299, 306, 376, 428, 515, 666, 1310, 2611])
assert_almost_equal(mstats.winsorize(data, (0.2, 0.2)).var(ddof=1), 21551.4, 1)
data[5] = masked
winsorized = mstats.winsorize(data)
assert_equal(winsorized.mask, data.mask)
def test_winsorization(self):
data = ma.array([77, 87, 88,114,151,210,219,246,253,262,
296,299,306,376,428,515,666,1310,2611])
assert_almost_equal(mstats.winsorize(data,(0.2,0.2)).var(ddof=1),
21551.4, 1)
data[5] = masked
winsorized = mstats.winsorize(data)
assert_equal(winsorized.mask, data.mask)
def standardize(df):
'''
:param df: (pandas dataframe) dataframe with columns: years, index: permnos, entries: metric_results
:return: (pandas dataframe) winsorized dataframe of z_scores
'''
# Keep the middle 95% of data. Clip the rest. NaN's in data will change percentiles.
winsorize(df, (0.025, 0.025), inplace=True)
z_score = (df - df.mean()) / df.std()
return z_score
def normalize_image_0_1_by_3channel(X, y):
print('--- Normalizing by channel, expecting 3 channel input ---')
assert X.shape[4] == 3
X_winsor = np.copy(X)
X_winsor[:, :, :, :, 0] = mstats.winsorize(X[:, :, :, :, 0], [0, 0.1])
X_winsor[:, :, :, :, 1] = mstats.winsorize(X[:, :, :, :, 1], [0, 0.1])
X_winsor[:, :, :, :, 2] = mstats.winsorize(X[:, :, :, :, 2], [0, 0.1])
X_norm = X_winsor - X_winsor.min()
X_norm = X_norm / X_winsor.std()
return X_norm, y
def playerdistribution(player, quantile=0.75):
jc = players21.loc[players21['Player'] == name][['GameFP/36', 'MIN']]
fp = winsorize(jc['GameFP/36'], [0.05, 0.05]).mean()
minutes = winsorize(jc['MIN'], [0.05, 0.05]).mean()
cj = jc.T
covariance = np.cov(cj)
distribution = pd.DataFrame(
multivariate_normal.rvs(mean=[fp, minutes], cov=covariance, size=1000))
distribution['total'] = distribution[0] / 36 * distribution[1]
graph = sb.kdeplot(data=distribution['total'], fill=True)
return graph, distribution['total'].quantile(quantile), covariance
def wincor(x, y, tr=.2):
"""
Compute the winsorized correlation between `x` and `y`.
This function also returns the winsorized covariance.
:param x: Pandas Series
Data for group one
:param y: Pandas Series
Data for group two
:param tr: float
Proportion to winsorize (default is .2)
:return:
Dictionary of results
cor: float
Winsorized correlation
nval: int
Number of observations
sig: float
p-value
wcov: float
Winsorized covariance
"""
if type(x) is not np.ndarray:
x, y=pandas_to_arrays([x, y])
m1 = np.c_[x, y] # cbind
m1 = m1[~np.isnan(m1).any(axis=1)]
nval = m1.shape[0]
x = m1[:, 0]
y = m1[:, 1]
g = np.floor(tr * len(x))
xvec = winsorize(x, limits=(tr,tr))
yvec = winsorize(y, limits=(tr,tr))
wcor = np.corrcoef(xvec, yvec)[0,1]
wcov = np.cov(xvec, yvec)[0,1]
test = wcor * np.sqrt((len(x) - 2) / (1. - wcor ** 2))
sig = 2 * (1 - t.cdf(abs(test), len(x) - 2 * g - 2))
res={'cor': wcor, 'wcov': wcov, 'sig': sig, 'nval': nval}
return res
def do_prepare_data():
main_df = pd.read_csv("tables/1_thirty_models.csv")
model_keys = main_df['model_key'].unique().tolist()
prepared_df = pd.DataFrame()
for model_key in model_keys:
df = load_data(main_df=main_df, model_key=model_key)
df = fill_missing_days__and_set_datetime_index(df,
start_date="2012-01-05",
end_date="2016-12-28")
df.to_csv("tables/_fill_days.csv")
df.loc[:, 'Order_Demand'] = mstats.winsorize(df['Order_Demand'].values,
limits=[0.05, 0.05])
# df['Order_Demand'] = df['Order_Demand'].apply(lambda x: np.log(x+1))
df = moving_average_imputation(df)
plot(df, model_key, '2_imputation_example')
df['model_key'] = df['Order_Demand'].apply(lambda x: model_key)
for i in range(1, 9):
df['lag_{}'.format(i)] = df['Order_Demand'].shift(i)
df = df.dropna()
df['Date'] = df.index
prepared_df = pd.concat([prepared_df, df])
prepared_df = sqldf(
"select model_key, Date, Order_Demand, lag_1, lag_2, lag_3, lag_4, lag_5, lag_6, lag_7, lag_8 from prepared_df ORDER BY 1,2",
locals())
prepared_df['Date'] = pd.to_datetime(prepared_df['Date'])
prepared_df.to_csv("tables/2_data_prepared.csv", index=False)
prepared_df.set_index(['Date'], inplace=True)
thirty_plots(df=prepared_df, filename="3_prepared_data")
def win_sor(self, limit): ''' 去极值函数''' ''' 替换极大或极小的因子值''' ''' 本质上修改了数据集''' ''' limit =0.1, 替换极大的百分之10数据和极小的百分之10数据''' self.data[self.feature] = winsorize(self.data[self.feature], limits=[limit, limit])
def future_spreads(spread_current, spread_history):
norm_reference = cma.val_dict['spread_norm_yrs']
future_spreads = pd.DataFrame(spread_current).T
future_spreads = future_spreads.reset_index(drop=True)
# Winsorize spread norm
spread_norm = []
for i in range(len(spread_history.columns)):
list = mstats.winsorize(spread_history.iloc[:, i].dropna(),
limits=[0.05, 0.05],
inclusive=[True, True])
def Average(lst):
return sum(lst) / len(lst)
spread_norm.append(Average(list))
# Create shell dataframe
for i in range(1, 11):
future_spreads.loc[i] = np.nan
# Calculate normalized spread path
for i in range(norm_reference, 11):
future_spreads.iloc[i, :] = spread_norm
# Populate data for years leading up to normalization
for i in range(1, norm_reference):
future_spreads.iloc[i, :] = (
future_spreads.iloc[norm_reference, :] - future_spreads.iloc[0, :]
) / norm_reference + future_spreads.iloc[i - 1, :]
return future_spreads
def cs_winFn(ser, level):
indx = ser.index
ser = ser.dropna()
arr = array(ser)
arr = winsorize(arr, limits=(level, level))
ser = Series(arr, index=ser.index)
return ser.reindex(indx)
def _sub(sub):
# winsorize returns an numpy array, sub is a dataframe; sub[:] replaces the "values" of the dataframe,
# not the dataframe itself
sub[:] = mstats.winsorize(a=sub.values,
limits=winsorize_bounds,
axis=0)
return sub
def winvar(x, tr=.2):
"""
Compute the gamma Winsorized variance for the data in the vector x.
tr is the amount of Winsorization which defaults to .2.
Nan values are removed.
:param x:
:param tr:
:return:
"""
y=winsorize(x, limits=(tr,tr))
wv = np.var(y, ddof=1)
# x=x[~np.isnan(x)]
# y=np.sort(x)
# n=len(x)
# ibot = int(np.floor(tr * n))
# itop = len(x) - ibot -1
# xbot = y[ibot]
# xtop = y[itop]
# y = np.where(y <= xbot, xbot, y)
# y = np.where(y >= xtop, xtop, y)
# wv = np.var(y, ddof=1) # DF to be consistent with Wilcox/R
return wv
def get_norm_side(mean, vol, ret, z):
side = winsorize(ret, limits=[0.025, 0.025])
side[(ret - mean) / np.sqrt(vol) > z] = 1
side[(ret - mean) / np.sqrt(vol) < -z] = -1
side[((ret - mean) / np.sqrt(vol) >= -z)
& ((ret - mean) / np.sqrt(vol) <= z)] = 0
return side
def treat_outliers(dataframe):
cols = list(dataframe)
for col in cols:
if col in dataframe.select_dtypes(include=np.number).columns:
dataframe[col] = winsorize(dataframe[col], limits=[0.05, 0.1],inclusive=(True, True))
return dataframe
def et(ax, d):
print "ET-------------"
A = np.transpose(d.jx)
(lines, cols) = np.shape(A)
window = np.hamming(lines).reshape(lines, 1)
#A *= window
ny, nx = np.shape(A)
#print "shape:", np.shape(A)
# configuration space parameter
#x = np.arange(nx)*dx
x = d.x
#print "x:"
#print x
x1 = 1
x2 = nx
#temporal guiding vector
#t = np.arange(ny)*dt
t = d.time
#print "t:"
#print t
t1 = 0
t2 = ny
# Change to spectra by considering |F]
#F = A*A #energy
F = A
print "min/max:", np.min(F), np.max(F)
X, T = np.meshgrid(x, t)
#print "nx=",nx
#print "ny=",ny
#print np.shape(X)
#print np.shape(T)
#slow (but more flexible) pcolormesh that takes guiding grid
#im = ax.pcolormesh(K[w1:w2, k1:k2], W[w1:w2, k1:k2], F[w1:w2, k1:k2],
# cmap='plasma',
# #vmin=ff.min(),
# #vmax=ff.max(),
# )
F = mstats.winsorize(F, limits=[0.01, 0.01])
vminmax = np.maximum(np.abs(np.min(F)), np.abs(np.max(F)))
#faster plotting with imshow
im = ax.imshow(F[t1:t2, x1:x2],
extent=[x[x1], x[x2 - 1], t[t1], t[t2 - 1]],
origin='lower',
aspect='auto',
interpolation='nearest',
cmap='RdYlGn',
vmin=-vminmax,
vmax=vminmax)
return F
def tobc(curr_arr, block_size, central_tendency, cutting_ratio, percentile):
answer = []
for curr_col in range(np.shape(curr_arr)[1]):
curr_row = 0
curr_col_moving = curr_col
sum_ = 0
median_list = []
while curr_row < np.shape(curr_arr)[0]:
sum_ = sum_ + curr_arr[curr_row][curr_col_moving]
median_list.append(curr_arr[curr_row][curr_col_moving])
curr_col_moving = curr_col_moving + 1
if curr_col_moving == np.shape(curr_arr)[1]:
curr_col_moving = 0
curr_row = curr_row + 1
if central_tendency == "sum":
temp_var = sum_
if central_tendency == "mean":
temp_var = sum_ / block_size[0]
if central_tendency == "median":
temp_var = median(median_list)
if central_tendency == "trim_mean":
temp_var = stats.trim_mean(np.array(median_list), cutting_ratio)
if central_tendency == "percentile":
temp_var = np.percentile(np.array(median_list), percentile)
if central_tendency == "win_mean":
temp_var = mean(winsorize(np.array(median_list), cutting_ratio))
answer.append(temp_var)
mid_answer = np.copy(circulant(answer).transpose())
return mid_answer
def preprocess(a):
a = a.astype(np.float64)
a[np.isinf(a)] = np.nan
a = np.nan_to_num(a - np.nanmean(a))
a = winsorize(a, limits=[WIN_LIMIT, WIN_LIMIT])
return preprocessing.scale(a)
def preprocess(a):
a = a.astype(np.float64)
a[np.isinf(a)] = np.nan
a = np.nan_to_num(a - np.nanmean(a))
a = winsorize(a, limits=[0.02,0.98])
return a
def normalize_image_0_1_by_6channel(X, y):
print('--- Normalizing by channel, expecting 6 channel input ---')
assert X.shape[4] == 6
X_winsor = np.copy(X)
# Only winsorize across 0 1 2, since 3 4 5 is already [0 1]
X_winsor[:, :, :, :, 0] = mstats.winsorize(X[:, :, :, :, 0], [0, 0.1])
X_winsor[:, :, :, :, 1] = mstats.winsorize(X[:, :, :, :, 1], [0, 0.1])
X_winsor[:, :, :, :, 2] = mstats.winsorize(X[:, :, :, :, 2], [0, 0.1])
X_norm = np.copy(X_winsor)
X_norm[:, :, :, :,
0:3] = X_winsor[:, :, :, :, 0:3] - X_winsor[:, :, :, :, 0:3].min()
X_norm[:, :, :, :, 0:3] = np.divide(X_norm[:, :, :, :, 0:3],
X_winsor[:, :, :, :, 0:3].std(),
where=X_winsor[:, :, :, :, 0:3] > 0)
return X_norm, y
def vectorWinsorize(input_v, limits=0.5):
'''
calculate z-score of each element in input_v to mean(input_v)
'''
result = np.copy(input_v)
result = winsorize(result, limits=limits)
return result
def winsorize_df(self, train_df, test_df, valid_df, cols, lower, upper, test_set, valid_set):
"""Function to winsorize numeric values in a DataFrame to remove potential outliers
Parameters
----------
train_df : pandas.DataFrame
DataFrame containing the training data
test_df : pandas.DataFrame
DataFrame containing the test data
train_df : pandas.DataFrame
DataFrame containing the training data
valid_df : pandas.DataFrame
DataFrame containing the validation data
cols : list
list of columns to winsorize
lower : int
Lower value e.g. 0.05 will cap the data at the 5th percentile
upper : int
Upper value e.g. 0.05 will cap the data at the 95th percentile
test_set : bool
Boolean indicating if a test set has been provided
valid_set : bool
Boolean indicating if a validation set has been provided
Returns
-------
train_df : pandas.DataFrame
DataFrame containing the winsorized training data
test_df : pandas.DataFrame
DataFrame containing the winsorized test data
valid_df : pandas.DataFrame
DataFrame containing the winsorized validation data
"""
for i in range(0, len(cols)):
winsor = pd.DataFrame(winsorize(train_df[cols[i]], limits=(lower, upper)))
winsor.columns = [cols[i]]
winsor_min = winsor[cols[i]].min()
winsor_max = winsor[cols[i]].max()
# Replace the column with the winsorized version for the training data
train_df.drop(cols[i], axis=1)
pd.concat([train_df, winsor], axis=1)
# Now replace in the test and validation sets using the values from the training data
if test_set:
test_df.loc[test_df[cols[i]] > winsor_max, cols[i]] = winsor_max
test_df.loc[test_df[cols[i]] < winsor_min, cols[i]] = winsor_min
if valid_set:
valid_df.loc[valid_df[cols[i]] > winsor_max, cols[i]] = winsor_max
valid_df.loc[valid_df[cols[i]] < winsor_min, cols[i]] = winsor_min
return train_df, test_df, valid_df
def Winsorize(self, df, colname, tile):
dfpivot2 = df[colname].astype(float).values
mask = np.isnan(dfpivot2)
wnp = mstats.winsorize(dfpivot2,
limits=[tile, tile],
axis=0,
inplace=True)
wnp[mask] = np.nan #Inplace true will fill all np.nan value with extreme value
df[colname] = wnp
return (df)
def transform(self, X):
if self.method == 'winsorize':
for col in X.columns.tolist():
X.loc[:, col] = mstats.winsorize(X[col],
limits=(self.low, self.high))
elif self.method == 'ceilfloor':
for col in X.columns.tolist():
# settingwithcopy warning
X.loc[X[col] < self.low, col] = self.low
X.loc[X[col] > self.high, col] = self.high
return X
def transform(self, X):
data = X.copy()
lim_inf_1 = len(data[data['NOTA_DE']==0.0])/len(data)
lim_sup_1 = len(data[data['NOTA_DE']> 10.0])/len(data)
data['NOTA_DE'] = winsorize(data['NOTA_DE'], limits=[lim_inf_1, lim_sup_1] )
lim_inf_2 = len(data[data['NOTA_EM']==0.0])/len(data)
lim_sup_2 = len(data[data['NOTA_EM']> 10.0])/len(data)
data['NOTA_EM'] = winsorize(data['NOTA_EM'], limits=[lim_inf_2, lim_sup_2] )
lim_inf_3 = len(data[data['NOTA_MF']==0.0])/len(data)
lim_sup_3 = len(data[data['NOTA_MF']> 10.0])/len(data)
data['NOTA_MF'] = winsorize(data['NOTA_MF'], limits=[lim_inf_3, lim_sup_3] )
lim_inf_4 = len(data[data['NOTA_GO']==0.0])/len(data)
lim_sup_4 = len(data[data['NOTA_GO']> 10.0])/len(data)
data['NOTA_GO'] = winsorize(data['NOTA_GO'], limits=[lim_inf_4, lim_sup_4] )
return data
def ols_beta(df, window):
model = pd.stats.ols.MovingOLS(y=df.ic,
x=df[['ih']],
window_type='rolling',
window=window,
intercept=True)
df['ols_beta'] = model.beta.ih.shift(1)
df['ols_r2'] = model.r2
df.ols_beta = mstats.winsorize(df.ols_beta, limits=[0.01, 0.01])
#df = df.replace([np.inf, -np.inf], np.nan).dropna()
return df
def _winsorize(a, limits=None, inclusive=(True, True)):
# drop masked data
a1 = np.ma.compressed(a)
# use .data to return an np.ndarray instead of a masked array
try:
wa = winsorize(a1, limits=limits, inclusive=inclusive).data
except IndexError:
wa = np.zeros(0, dtype=a.dtype)
return wa
def fm_regression(self):
data = self.cache_data.loc[:, ['forward_return'] + self.cache['factor_names']].copy()
# need to winsorize
for factor in self.cache['factor_names']:
data[factor] = data[factor].apply(lambda x: winsorize(x, (0.25, 0.25)))
# cross-sectional regression
# time-series regression
# test
pass
def winsorize_norm_chromosome_data(read_5p_ends, chromosome, strand, genome_dict, nucs_to_count, to_winsorize = True, low = 0, high = 0.95):
"""
:param read_5p_ends:
:param chromosome:
:param strand:
:param genome_dict:
:param nucs_to_count:
:param low:
:param high:
:return: an array (now zero-indexed from 1-indexed) of densities for the given chromosome on the given strand, winsorized, and only for the given nucleotides
"""
max_position = max(read_5p_ends[strand][chromosome].keys())
density_array =numpy.array([0] * max_position)
for position in read_5p_ends[strand][chromosome].keys():
if genome_dict[chromosome][position-1] in nucs_to_count:
density_array[position-1] = read_5p_ends[strand][chromosome][position]
if to_winsorize:
winsorize(density_array, limits = (low, 1-high), inplace = True)
normed_array = density_array/float(max(density_array))
return normed_array
def normalize_dict_to_max(mutation_dict, winsorize_data = False, winsorization_limits = (0, 0.95)):
all_values = []
normed_dict = {}
for strand in mutation_dict:
normed_dict[strand] = {}
for chromosome in mutation_dict[strand]:
normed_dict[strand][chromosome] = {}
#print mutation_dict[strand][chromosome].values()
all_values += mutation_dict[strand][chromosome].values()
#print all_values
if winsorize_data:
winsorize(all_values, limits = (winsorization_limits[0], 1-winsorization_limits[1]), inplace = True)
max_value = float(max(all_values))
for strand in mutation_dict:
for chromosome in mutation_dict[strand]:
for position in mutation_dict[strand][chromosome]:
val = mutation_dict[strand][chromosome][position]
if val < min(all_values):
val = min(all_values)
if val > max(all_values):
val = max(all_values)
normed_dict[strand][chromosome][position] = val/max_value
return normed_dict
def fit_transform(self, X):
self.fitted = True
sizeSig = np.size(X,1)/self.cardElec
for numElec in range(self.cardElec):
elecSlice = slice(numElec*sizeSig,(numElec+1)*sizeSig,1)
test = X[:,elecSlice]
X[:,elecSlice] = winsorize(X[:,elecSlice],limits=self.limit)
test = X[:,elecSlice]
self.maximum = np.max(X)
self.minimum = np.min(X)
return X
def winsorize_series(group,trim_prop):
return mstats.winsorize(group, limits=[trim_prop,trim_prop]).mean()
def winsor(s):
return mstats.winsorize(s, limits=[0.01, 0.01])