def on_draw(self):
plt.sca(self.ax)
plt.clf()
self.ax = plt.axes()
if self.df is None:
message = "Select two or more variables from list"
self.ax.text(0.5,
0.5,
message,
horizontalalignment='center',
verticalalignment='center',
fontsize=16)
else:
plt.sca(self.ax)
sns.corrplot(self.df,
annot=False,
sig_stars=True,
cmap_range="full",
diag_names=False,
sig_corr=False,
cmap=self.cmap,
ax=self.ax,
cbar=True)
plt.tight_layout()
self.draw()
def res_matrix(mark,state,cut_off=40):
path = os.path.join(get_data_dir(), "tmp", "{0} in {1}-{2}.csv".format(mark, state,cut_off))
DF = pd.read_csv(path, sep='\t')
Full_EID_list = get_full_EID_list()
res_matrix = []
tmp = [0.]*len(Full_EID_list)
for i in range(0,len(DF.index),1):
try:
if DF.chromMiddle[i-1] == DF.chromMiddle[i]:
tmp[Full_EID_list.index(DF.EID[i])] = DF.signalValue[i]
else:
res_matrix.append(tmp)
tmp = [0.]*len(Full_EID_list)
except:
pass
f, ax = plt.subplots(figsize=(15, 15))
cmap = sns.diverging_palette(210, 10, as_cmap=True)
sns.corrplot(np.array(res_matrix), annot=False, sig_stars=False, # .T??
diag_names=False, cmap=cmap, ax=ax)
f.tight_layout()
plt.show()
path2 = os.path.join(get_data_dir(), "tmp","{0} in {1}-{2}_diff.csv".format(mark,state,cut_off))
a = open(path2,'w')
for i in range(0,len(res_matrix[0]),1):
for j in range(0,len(res_matrix),1):
a.write(str(res_matrix[j][i])+"\t")
a.write("\n")
a.close()
def make_plot(X_train, y_train, X, y, test_data, model, model_name, features, response):
feature = X.columns
f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharey=False)
sns.regplot(X[feature[4]], y, test_data, ax=ax1)
sns.boxplot(X[feature[4]], y, color="Blues_r", ax=ax2)
model.fit(X_train, y_train)
sns.residplot(X[feature[4]], (model.predict(X) - y) ** 2, color="indianred", lowess=True, ax=ax3)
if model_name is 'linear':
sns.interactplot(X[feature[3]], X[feature[4]], y, ax=ax4, filled=True, scatter_kws={"color": "dimgray"}, contour_kws={"alpha": .5})
elif model_name is 'logistic':
pal = sns.blend_palette(["#4169E1", "#DFAAEF", "#E16941"], as_cmap=True)
levels = np.linspace(0, 1, 11)
sns.interactplot(X[feature[3]], X[feature[4]], y, levels=levels, cmap=pal, logistic=True)
else:
pass
ax1.set_title('Regression')
ax2.set_title(feature[4]+' Value')
ax3.set_title(feature[4]+' Residuals')
ax4.set_title('Two-value Interaction')
f.tight_layout()
plt.savefig(model_name+'_'+feature[4], bbox_inches='tight')
# Multi-variable correlation significance level
f, ax = plt.subplots(figsize=(10, 10))
cmap = sns.blend_palette(["#00008B", "#6A5ACD", "#F0F8FF",
"#FFE6F8", "#C71585", "#8B0000"], as_cmap=True)
sns.corrplot(test_data, annot=False, diag_names=False, cmap=cmap)
ax.grid(False)
ax.set_title('Multi-variable correlation significance level')
plt.savefig(model_name+'_multi-variable_correlation', bbox_inches='tight')
# complete coefficient plot - believe this is only for linear regression
sns.coefplot("diagnosis ~ "+' + '.join(features), test_data, intercept=True)
plt.xticks(rotation='vertical')
plt.savefig(model_name+'_coefficient_effects', bbox_inches='tight')
def make_corr_plot(d, title="plot"):
f, ax = plt.subplots(figsize=(9, 9))
cmap = sns.diverging_palette(220, 10, as_cmap=True)
sns.corrplot(d, annot=False, sig_stars=False,
diag_names=False, cmap=cmap, ax=ax)
f.tight_layout()
plt.title(title)
f.savefig(title)
def computeCorrelation(dataFrame, candidatesList,name):
fig, axes = plt.subplots(figsize=(12,12))
dfCorr = dataFrame[candidatesList]
cmap = sb.blend_palette(["#6B229F", "#FD3232", "#F66433",
"#E78520", "#FFBB39"], as_cmap=True)
sb.corrplot(dfCorr, annot=False, sig_stars=False,
diag_names=False, cmap=cmap)
axes.set_title("Correlation Matrix - " + name )
plt.savefig('Correlation_'+candidatesList[0]+'_.png')
def corrplot(mod_dis):
df_model = []
for label, data in mod_dis.items():
inds = np.triu_indices(data.shape[0], k=1)
df_model.append(data[inds])
df_model = pandas.DataFrame(np.array(df_model).T,
columns=mod_dis.keys())
sns.corrplot(df_model)
def correlateRDMs(allrsasimspaces, models):
spaces = []
for m in models:
spaces.append(np.array(allrsasimspaces[m]['simmat_across']).flatten())
spaces = np.array(spaces)
spaces = pd.DataFrame(data={model: spaces[modeln] for modeln, model in enumerate(models)})
spaces = spaces[models]
f, ax = plt.subplots(figsize=[12, 12])
sns.corrplot(spaces, diag_names=False, sig_stars=False)
return spaces.corr()
def correlations(data, X):
X_title = "_".join([i for i in X.columns.tolist()])
f, ax = plt.subplots(figsize=(10, 10))
cmap = sns.blend_palette(["#00008B", "#6A5ACD", "#F0F8FF",
"#FFE6F8", "#C71585", "#8B0000"], as_cmap=True)
sns.corrplot(data, annot=False, diag_names=False, cmap=cmap)
ax.grid(False)
plt.savefig('visuals/'+X_title+'_correlation')
print('visuals/'+X_title+'_correlation')
plt.close()
def seaborn_plot(df,plot_type='pairplot',columns=False):
sns.set()
mpl.rc("figure", figsize=(16, 8.65))
plotting_df=(df[columns] if columns else df)
if plot_type=='pairplot':
sns.pairplot(plotting_df)
elif plot_type=='corr_plot':
sns.corrplot(plotting_df)
sns.plt.show()
return
def seaborn_plot(df, columns, plot_type='pairplot'):
sns.set()
mpl.rc("figure", figsize=(16, 8.65))
plotting_df = df[columns]
if plot_type == 'pairplot':
sns.pairplot(plotting_df)
elif plot_type == 'corr_plot':
sns.corrplot(plotting_df)
sns.plt.show()
return
def visualize_correlations(training_data):
"""
Generates a correlation matrix heat map.
"""
fig, ax = plt.subplots(figsize=(16, 10))
colormap = sb.blend_palette(sb.color_palette('coolwarm'), as_cmap=True)
if len(training_data.columns) < 30:
sb.corrplot(training_data, annot=True, sig_stars=False, diag_names=True, cmap=colormap, ax=ax)
else:
sb.corrplot(training_data, annot=False, sig_stars=False, diag_names=False, cmap=colormap, ax=ax)
fig.tight_layout()
def l_reg(input_path):
DF = pd.read_csv(input_path)
DF.drop('gene_id', axis=1, inplace=True)
#corr_mat = np.corrcoef(DF.as_matrix())
f, ax = plt.subplots(figsize=(20, 20))
cmap = sns.diverging_palette(220, 10, as_cmap=True)
sns.corrplot(DF.as_matrix().T, annot=False, sig_stars=False,
diag_names=False, cmap=cmap, ax=ax)
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
plt.savefig(os.path.join(get_data_dir(), "tmp", "H3K27me3_corrplot.png"))
def corrplot(ax):
rs = np.random.RandomState(0)
x0, x1 = rs.randn(2, 60)
x2, x3 = rs.multivariate_normal([0, 0], [(1, -.5), (-.5, 1)], 60).T
x2 += x0 / 8
x4 = x1 + rs.randn(60) * 2
data = np.c_[x0, x1, x2, x3, x4]
sns.corrplot(data, ax=ax)
ax.set_title("corrplot()", verticalalignment="top")
def plot_pt_corr(df):
"""
plot the correlation matrix of the posteriors of the parameters
"""
f, ax = P.subplots(figsize=(9, 9))
cmap = sns.blend_palette(["#00008B", "#6A5ACD", "#F0F8FF",
"#FFE6F8", "#C71585", "#8B0000"], as_cmap=True)
sns.corrplot(df, annot=True, sig_stars=True, method='spearman',
diag_names=True, cmap=cmap, ax=ax)
f.tight_layout()
def corrplot(ax):
rs = np.random.RandomState(0)
x0, x1 = rs.randn(2, 60)
x2, x3 = rs.multivariate_normal([0, 0], [(1, -.5), (-.5, 1)], 60).T
x2 += x0 / 8
x4 = x1 + rs.randn(60) * 2
data = np.c_[x0, x1, x2, x3, x4]
sns.corrplot(data, ax=ax)
ax.set_title("corrplot()", verticalalignment="top")
def get_cor_matrix( self,method="pearson" ):
self.method = method
out_cor_file = "%s.corMat.%s.pdf" % ( ".".join( self.infile.split(".")[:-2] ), self.method )
pd_mat = pd.DataFrame( self.mat.matrix )
pd_mat.columns = self.mat.colname
pd_mat.index = self.mat.rowname
self.cor_mat = pd_mat.corr( self.method ).values
sns.set(style="darkgrid")
f, ax = plt.subplots(figsize=(9, 9))
cmap = sns.diverging_palette(220, 10, as_cmap=True)
sns.corrplot(pd_mat, annot=False, sig_stars=False, diag_names=False, cmap=cmap, ax=ax, cmap_range=(0.0, 1.0),method=self.method )
f.savefig( out_cor_file,format="pdf" )
def plot_correlations(df, include_numbers, filename):
'''
Plot maps of cross correlations of input and output variables.
'''
plt.figure()
sns.set()
sns.corrplot(df, annot=include_numbers)
plt.savefig(filename)
plt.close()
return
#=====================================================================
# EOF
#=====================================================================
def corrplot_example():
"""
Birds-eye view of a large dataset to see correlation matrix with a
heat map. Also gets a permutationt test to get p values. If you
have a huge dataset, will take a while and p values aren't relevant.
"""
titanic = sns.load_dataset("titanic").dropna() # load dataset1
attention = sns.load_dataset("attention") # load dataset2
sns.set_context(rc={"figure.figsize": (8, 8)}) # set size
sns.corrplot(titanic) # plot dataset1
#sns.corrplot(titanic, # dataset
# sig_tail="upper", # specify if only want pos or neg values
# cmap_range(-.3, 0)) # specify colormap range
sns.corrplot(attention) # plot dataset2
plt.show()
def corrplot_example():
"""
Birds-eye view of a large dataset to see correlation matrix with a
heat map. Also gets a permutationt test to get p values. If you
have a huge dataset, will take a while and p values aren't relevant.
"""
titanic = sns.load_dataset("titanic").dropna() # load dataset1
attention = sns.load_dataset("attention") # load dataset2
sns.set_context(rc={"figure.figsize": (8,8)}) # set size
sns.corrplot(titanic) # plot dataset1
#sns.corrplot(titanic, # dataset
# sig_tail="upper", # specify if only want pos or neg values
# cmap_range(-.3, 0)) # specify colormap range
sns.corrplot(attention) # plot dataset2
plt.show()
def plot_corr_matrix(X) :
"""
Plots correlation matrix for data
"""
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="white")
labels = ["radar dist",
"Ref",
"Ref 5x5 10th",
"Ref 5x5 50th",
"Ref 5x5 90th",
"RefComposite",
"RefComposite 5x5 10th",
"RefComposite 5x5 50th",
"RefComposite 5x5 90th",
"Rho_HV",
"Rho_HV 5x5 10th",
"Rho_HV 5x5 50th",
"Rho_HV 5x5 90th",
"Zdr",
"Zdr 5x5 10th",
"Zdr 5x5 50th",
"Zdr 5x5 90th",
"Kdp",
"Kdp 5x5 10th",
"Kdp 5x5 50th",
"Kdp 5x5 90th",
"Expected"]
d = pd.DataFrame(data=X[:,2:].copy(), columns=labels)
# Compute the correlation matrix
corr = d.corr()
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(11, 9))
sns.corrplot(d)
ax.set_title('Correlation Matrix for Radar Features and Output Variable')
ax.set_xlabel('Features (along diagonal)')
ax.set_ylabel('Correlation Values (upper triangle)')
f.tight_layout()
plt.show()
def preplot(df):
_ = sns.pairplot(df[:50], vars = [8, 11, 12, 14, 19], hue = 'class', size = 1.5)
plt.show()
plt.figure(figsize = (12, 10))
_ = sns.corrplot(df, annot = False)
plt.show()
def build_corrmatrix_dashboard(train_pre):
plt = sns.corrplot(train_pre, annot=False)
#sns.corrplot(train_pre)
print 'Saving correlation matrix in figures/.'
plt.savefig("figures/corr_matrix.png")
def build_corrmatrix_dashboard(train_pre):
plt = sns.corrplot(train_pre,annot=False)
#sns.corrplot(train_pre)
print 'Saving correlation matrix in figures/.'
plt.savefig("figures/corr_matrix.png")
def attribute_correlations(df, img_file='attr_correlations.png'):
logging.debug('Plotting attribute pairwise correlations')
# custom figure size (in inches) to cotrol the relative font size
fig, ax = plt.subplots(figsize=(10, 10))
# nice custom red-blue diverging colormap with white center
cmap = sns.diverging_palette(250, 10, n=3, as_cmap=True)
# Correlation plot
# - attribute names on diagonal
# - color-coded correlation value in lower triangle
# - values and significance in the upper triangle
# - color bar
# If there a lot of attributes we can disable the annotations:
# annot=False, sig_stars=False, diag_names=False
sns.corrplot(df, ax=ax, cmap=cmap)
# remove white borders
fig.tight_layout()
fig.savefig(img_file)
plt.close(fig)
def display_corr_matrix():
'''
function plots a correlation matrix heat map
'''
global DF
### create a correlation matrix heatmap to look for colinearity
data = DF
sns.set(color_codes=True)
f, ax = plt.subplots(figsize=(9, 9))
cmap = sns.blend_palette(["#00008B", "#6A5ACD", "#F0F8FF",
"#FFE6F8", "#C71585", "#8B0000"], as_cmap=True)
sns.corrplot(data, annot=False, sig_stars=False,
diag_names=False, cmap=cmap, ax=ax)
sns.plt.title('Figure 1: Correlation Matrix Heatmap')
f.tight_layout()
sns.despine()
sns.plt.show()
def attribute_correlations(df, img_file='attr_correlations.png'):
logging.debug('Plotting attribute pairwise correlations')
# custom figure size (in inches) to cotrol the relative font size
fig, ax = plt.subplots(figsize=(10, 10))
# nice custom red-blue diverging colormap with white center
cmap = sns.diverging_palette(250, 10, n=3, as_cmap=True)
# Correlation plot
# - attribute names on diagonal
# - color-coded correlation value in lower triangle
# - values and significance in the upper triangle
# - color bar
# If there a lot of attributes we can disable the annotations:
# annot=False, sig_stars=False, diag_names=False
sns.corrplot(df, ax=ax, cmap=cmap)
# remove white borders
fig.tight_layout()
fig.savefig(img_file)
plt.close(fig)
def plotSocialMedia(df):
df = df.apply(pd.to_numeric, errors='ignore')
print(df.info())
fig, ax = plt.subplots(figsize=(10, 10))
sns.corrplot(df, ax=ax)
plt.savefig('socialNetworkCorrelations.png', tight_layout=True)
plt.close()
# mean session duration
df.sort_values("avgSessionDuration", ascending=False, inplace=True)
g = sns.barplot(y='socialNetwork', x='avgSessionDuration', hue='userType', data=df)
g.set(xlabel='Average Session Duration', ylabel='')
plt.subplots_adjust(left=.17)
sns.despine(left=True, bottom=True)
plt.savefig('socialNetworkAvgDuration.png')
plt.close()
# bounceRate
df.sort_values("bounceRate", inplace=True)
g = sns.barplot(y='socialNetwork', x='bounceRate', hue='userType', data=df)
g.set(xlabel='Bounce Rate', ylabel='')
plt.subplots_adjust(left=.17)
sns.despine(left=True, bottom=True)
plt.savefig('socialNetworkBounceRate.png')
plt.close()
# avgTimeOnPage
df.sort_values("avgTimeOnPage", ascending=False, inplace=True)
g = sns.barplot(y='socialNetwork', x='avgTimeOnPage', hue='userType', data=df)
g.set(xlabel='Average Time on Page', ylabel='')
plt.subplots_adjust(left=.17)
sns.despine(left=True, bottom=True)
plt.savefig('socialNetworkavgTimeOnPage.png')
plt.close()
# goals completed
df.sort_values("goalCompletionsAll", ascending=False, inplace=True)
g = sns.barplot(y='socialNetwork', x='goalCompletionsAll', hue='userType', data=df)
g.set(xlabel='Goal Completions', ylabel='', xscale='log')
plt.subplots_adjust(left=.17)
sns.despine(left=True, bottom=True)
plt.savefig('socialNetworkgoalCompletionsAll.png')
plt.close()
def get_feature_corr(df_k, keepers, scale_it=True):
if scale_it:
X_k = scale(np.array(df_k, dtype=float))
else:
X_k = np.array(df_k, dtype=float)
df_xk = pd.DataFrame(X_k)
df_xk.columns = keepers
sns.set(style="darkgrid")
fig, ax = plt.subplots(figsize=(15, 15))
cmap = sns.diverging_palette(220, 10, as_cmap=True)
sns.corrplot(df_xk,
annot=True,
sig_stars=True,
diag_names=False,
cmap=cmap,
ax=ax)
ax.set_title('Correlation between training features')
fig.tight_layout()
return fig
def fig_correlations(data, aly_title, fig_save = True):
""" Plot correlations
Parameters
----------
data : pd.DataFrame
aly_title : str
fig_save : bool, optional
False if data should not be saved
"""
ff = file_folder_specs()
plt.figure()
sns.corrplot(data, diag_names = False)
plt.title(aly_title)
if fig_save:
_save_fig(aly_title, ff['fig'])
plt.show()
plt.close()
def visualize_correlations(training_data):
"""
Generates a correlation matrix heat map.
"""
fig, ax = plt.subplots(figsize=(16, 10))
colormap = sb.blend_palette(sb.color_palette('coolwarm'), as_cmap=True)
if len(training_data.columns) < 30:
sb.corrplot(training_data,
annot=True,
sig_stars=False,
diag_names=True,
cmap=colormap,
ax=ax)
else:
sb.corrplot(training_data,
annot=False,
sig_stars=False,
diag_names=False,
cmap=colormap,
ax=ax)
fig.tight_layout()
def tech_summary():
closing_df = DataReader(['AAPL', 'GOOG', 'MSFT', 'AMZN'], 'yahoo', start,
end)['Adj Close']
tech_rets = closing_df.pct_change()
# from IPython.display import SVG
# SVG(url='http://upload.wikimedia.org/wikipedia/commons/d/d4/Correlation_examples2.svg')
sns.jointplot('GOOG', 'GOOG', tech_rets, kind='scatter', color='seagreen')
sns.jointplot('GOOG', 'MSFT', tech_rets, kind='scatter')
sns.pairplot(tech_rets.dropna())
# Set up our figure by naming it returns_fig, call PairPLot on the DataFrame
returns_fig = sns.PairGrid(tech_rets.dropna())
# Using map_upper we can specify what the upper triangle will look like.
returns_fig.map_upper(plt.scatter, color='purple')
# We can also define the lower triangle in the figure, inclufing the plot type (kde) or the color map (BluePurple)
returns_fig.map_lower(sns.kdeplot, cmap='cool_d')
# Finally we'll define the diagonal as a series of histogram plots of the daily return
returns_fig.map_diag(plt.hist, bins=30)
# Set up our figure by naming it returns_fig, call PairPLot on the DataFrame
returns_fig = sns.PairGrid(closing_df)
# Using map_upper we can specify what the upper triangle will look like.
returns_fig.map_upper(plt.scatter, color='purple')
# We can also define the lower triangle in the figure, inclufing the plot type (kde) or the color map (BluePurple)
returns_fig.map_lower(sns.kdeplot, cmap='cool_d')
# Finally we'll define the diagonal as a series of histogram plots of the closing price
returns_fig.map_diag(plt.hist, bins=30)
# Let's go ahead and use sebron for a quick correlation plot for the daily returns
sns.corrplot(tech_rets.dropna(), annot=True)
return tech_rets
def get_cor_matrix(self, method="pearson"):
self.method = method
out_cor_file = "%s.corMat.%s.pdf" % (".".join(
self.infile.split(".")[:-2]), self.method)
pd_mat = pd.DataFrame(self.mat.matrix)
pd_mat.columns = self.mat.colname
pd_mat.index = self.mat.rowname
self.cor_mat = pd_mat.corr(self.method).values
sns.set(style="darkgrid")
f, ax = plt.subplots(figsize=(9, 9))
cmap = sns.diverging_palette(220, 10, as_cmap=True)
sns.corrplot(pd_mat,
annot=False,
sig_stars=False,
diag_names=False,
cmap=cmap,
ax=ax,
cmap_range=(0.0, 1.0),
method=self.method)
f.savefig(out_cor_file, format="pdf")
def explorer(data, name, hue=None, trel=True, corr=True):
"""
Draw and save Trellis plots including scatter plots (upper triangle) and kernal density (lower triangle and lower triangle), correlation map with person R and p value. Takes long time with big data.
Args:
data: dataFrame. Input data arrays.
name: str. Name of output figure file.
hue: str, optional. Name of variable used as hue.
Return:
PairGrid
"""
if name[-4:] == '.pdf':
mpl.use('PDF')
import matplotlib.pyplot as plt
#sns.set_context("talk", font_scale=1.3)
if trel:
print 'Plotting Trellis plots.'
#sns.set(style="white")
#f, ax = plt.subplots(figsize=(7, 7))
#ax.set(xscale="log", yscale="log")
g = sns.PairGrid(data, hue=hue)
g.map_lower(sns.kdeplot, cmap="Purples", shade=True)
g.map_diag(plt.hist)
g.map_upper(plt.scatter, s=10, alpha=.05)
g.savefig('trel_' + name, dpi=300)
plt.close()
if corr:
print 'Plotting correlation map.'
#sns.set_context(rc={"figure.figsize": (16, 16)})
plt.figure()
ax = sns.corrplot(data)
ax.figure.savefig('corr_' + name, dpi=300)
plt.close()
def explorer(data, name, hue=None, trel=True, corr=True):
"""
Draw and save Trellis plots including scatter plots (upper triangle) and kernal density (lower triangle and lower triangle), correlation map with person R and p value. Takes long time with big data.
Args:
data: dataFrame. Input data arrays.
name: str. Name of output figure file.
hue: str, optional. Name of variable used as hue.
Return:
PairGrid
"""
if name[-4:]=='.pdf':
mpl.use('PDF')
import matplotlib.pyplot as plt
#sns.set_context("talk", font_scale=1.3)
if trel:
print 'Plotting Trellis plots.'
#sns.set(style="white")
#f, ax = plt.subplots(figsize=(7, 7))
#ax.set(xscale="log", yscale="log")
g = sns.PairGrid(data, hue=hue)
g.map_lower(sns.kdeplot, cmap="Purples",shade=True)
g.map_diag(plt.hist)
g.map_upper(plt.scatter, s=10, alpha=.05)
g.savefig('trel_'+name, dpi = 300)
plt.close()
if corr:
print 'Plotting correlation map.'
#sns.set_context(rc={"figure.figsize": (16, 16)})
plt.figure()
ax = sns.corrplot(data)
ax.figure.savefig('corr_'+name, dpi = 300)
plt.close()
def plotpair(df):
# pr = sns.pairplot(df[:500], vars=['codeFragNum', 'liNum', 'popTagsNum',
# 'bodyLength', 'titleLength'],
# hue='class', size=1.5)
cor = sns.corrplot(df, annot=False)
print rawdata.cov()
print rawdata[['Age', 'Ca']].corr()
pd.DataFrame.corr(rawdata)
plt.show()
# define colors list, to be used to plot survived either red (=0) or green (=1)
colors = ['red', 'green']
# make a scatter plot
# rawdata.info()
from scipy import stats
import seaborn as sns # just a conventional alias, don't know why
sns.corrplot(rawdata) # compute and plot the pair-wise correlations
# save to file, remove the big white borders
#plt.savefig('attribute_correlations.png', tight_layout=True)
plt.show()
attr = rawdata['Age']
sns.distplot(attr)
plt.show()
sns.distplot(attr, kde=False, fit=stats.gamma)
plt.show()
# Two subplots, the axes array is 1-d
plt.figure(1)
plt.title('Histogram of Age')
plt.subplot(211) # 21,1 means first one of 2 rows, 1 col
def make_correlation_plot(df):
f, ax = plt.subplots(figsize=(12, 12))
sns.corrplot(df, annot=True, sig_stars=False,
diag_names=False, ax=ax)
sns.interactplot(x1, x2, y, colorbar=False, ax=ax)
# Correlation matrix
# ------------------
ax = plt.subplot(gs[4:, 0])
rs = np.random.RandomState(0)
x0, x1 = rs.randn(2, 60)
x2, x3 = rs.multivariate_normal([0, 0], [(1, -.5), (-.5, 1)], 60).T
x2 += x0 / 8
x4 = x1 + rs.randn(60) * 2
data = np.c_[x0, x1, x2, x3, x4]
sns.corrplot(data, ax=ax)
ax.set_title("corrplot()", verticalalignment="top")
# Beta distributions
# ------------------
sns.set(style="nogrid")
ax = plt.subplot(gs[4, 1])
plt.title("distplot()")
plt.xlim(0, 1)
ax.set_xticklabels([])
g, _, p = sns.color_palette("Set2", 3, desat=.75)
n = 1000
sns.pairplot(tech_rets.dropna())
plt.show()
returns_fig = sns.PairGrid(tech_rets.dropna())
returns_fig.map_upper(plt.scatter, color='purple')
returns_fig.map_lower(sns.kdeplot, cmap='cool_d')
returns_fig.map_diag(plt.hist, bins=30)
plt.show()
returns_fig = sns.PairGrid(closing_df)
returns_fig.map_upper(plt.scatter, color='purple')
returns_fig.map_lower(sns.kdeplot, cmap='cool_d')
returns_fig.map_diag(plt.hist, bins=30)
plt.show()
sns.corrplot(tech_rets.dropna(), annot=True)
plt.show()
sns.corrplot(closing_df, annot=True)
plt.show()
''' analyze the risk of a stock '''
rets = tech_rets.dropna()
area = np.pi * 20
plt.scatter(rets.mean(), rets.std(), s = area)
plt.xlabel('Expected Return')
plt.ylabel('Risk')
for label, x, y in zip(rets.columns, rets.mean(), rets.std()):
plt.annotate(
label,
dataset = load_boston() df = pd.DataFrame(dataset.data, columns=dataset.feature_names) df['target'] = dataset.target # correlation corr = df.corr(method='pearson') corr.sort_values(by = 'target', inplace = True) # find pairs with high correlations import seaborn as sns # just a conventional alias, don't know why fig, ax = plt.subplots(figsize=(10, 10)) sns.corrplot(df, ax = ax) fig, ax = plt.subplots(figsize=(10, 10)) sns.distplot(attr) import matplotlib.pyplot as plt attr = df.target plt.hist(attr) plt.scatter(df.target, df['LSTAT']) sns.jointplot(df.target, df['LSTAT'], kind='scatter') sns.jointplot(df.target, df['LSTAT'], kind='hex') ### explore some diagnostic plots: QQ
# In[16]: sns.pairplot(tech_rets.dropna()) # In[17]: returns_fig = sns.PairGrid(closing_df) returns_fig.map_upper(plt.scatter,color='purple') # the lower triangle in the figure, inclufing the plot type (kde) or the color map (BluePurple) returns_fig.map_lower(sns.kdeplot,cmap='cool_d') # a series of histogram plots of the closing price returns_fig.map_diag(plt.hist,bins=30) # In[18]: sns.corrplot(tech_rets.dropna(),annot=True) # In[ ]:
i+=1
#Dem most anticorrelated
sns_plot = sns.jointplot('Hillary Clinton','Bernie Sanders',pr_piv,kind='scatter')
sns_plot.savefig(OutputFolder+'HillaryClinton_BernieSanders_joinplot.png')
#Primary results assume a choice between Democrats candidates only or
#Republican candidates only
#So comparing Democrats to Republicans based on these results
#does not have a lot of sense
#However let's look on the picture as a whole
heatmap(rvalue,'rvalue.png')
#seabron for a quick correlation plot which is pandas.DataFrame.corr('pearson')
f, ax = plt.subplots(figsize=(15, 15))
sns_plot = sns.corrplot(pr_piv,annot=True, ax=ax)
plt.savefig(OutputFolder+'corrplot.png')
#Let's look now how high is the possibility of the correlation
#between democrat and republican candidates
#we can not trust such results
heatmap(pvalue,'pvalue.png')
#You can take a look at the StdError of the correlation as well
#heatmap(stderr,'stderr.png')
#Hillary Clinton to Republican
sns_plot = sns.jointplot('Hillary Clinton','Donald Trump',pr_piv,kind='scatter')
sns_plot.savefig(OutputFolder+'HillaryClinton_DonaldTrump_joinplot.png')
def extra_viz(loansData):
f, ax = plt.subplots(figsize=(10, 10))
sns.corrplot(loansData, ax=ax)
plt.savefig('../figs/loan_corr_matrix.png')
##Try Correlation and Corrplot to see what features popout more than others:
df.corr(method='pearson')
pearson = df.corr(method='pearson')
#print pearson
# assume target attr is the last, then remove corr with itself
corr_with_target = pearson.ix[-7][:-1]
#print pearson.ix
print corr_with_target
# correlations by the absolute value:
corr_with_target[abs(corr_with_target).argsort()[::-1]]
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(20, 15))
sns.corrplot(df) # compute and plot the pair-wise correlations
# save to file, remove the big white borders
plt.savefig('attribute_correlations.png', tight_layout=True)
##Use scikit-learn's SelectKBest feature selection:
def get_k_best(enron_data, features_list, k):
""" runs scikit-learn's SelectKBest feature selection
returns dict where keys=features, values=scores
"""
data = featureFormat(enron_data, features_list)
labels, features = targetFeatureSplit(data)
k_best = SelectKBest(k=k)
k_best.fit(features, labels)
scores = k_best.scores_
import numpy as np
import pandas as pd
from numpy.random import randn
import matplotlib.pyplot as plt
import seaborn as sns
from pandas import Series,DataFrame
array = np.array([[1,3,4,4],[1,4,5,5]])
dframe1 = DataFrame(array,index=list('AB'),columns=list('abcd'))
print(dframe1)
print(dframe1.describe())
import pandas.io.data as pdweb
import datetime
prices_oils = pdweb.get_data_yahoo(['CVX','XOM','BP'],start=datetime.datetime(2013,1,1),end=datetime.datetime(2016,1,1))['Adj Close']
print(prices_oils.head())
prices_volume = pdweb.get_data_yahoo(['CVX','XOM','BP'],start=datetime.datetime(2013,1,1),end=datetime.datetime(2016,1,1))['Volume']
prices_volume.head()
rets = prices_oils.pct_change()
corr = rets.corr
prices_oils.plot()
sns.corrplot(rets,annot=False,diag_names=False)
# We can simply call pairplot on our DataFrame for an automatic visual analysis of all the comparisons sns.pairplot(tech_rets.dropna()) # In[38]: returns_fig = sns.PairGrid(tech_rets.dropna()) returns_fig.map_upper(plt.scatter,color='purple') returns_fig.map_lower(sns.kdeplot,cmap='cool_d') returns_fig.map_diag(plt.hist,bins=30) # In[42]: sns.corrplot(tech_rets.dropna(),annot=True) # In[43]: sns.corrplot(closing_df,annot=True) # ## Risk Analysis # In[48]: # Let's start by defining a new DataFrame as a clenaed version of the oriignal tech_rets DataFrame rets = tech_rets.dropna() area = np.pi*20 plt.scatter(rets.mean(), rets.std(),alpha = 0.5,s =area)
returns_fig = sns.PairGrid(tech_rets.dropna()) returns_fig.map_upper(plt.scatter, color='purple') returns_fig.map_lower(sns.kdeplot, cmap='cool_d') returns_fig.map_diag(plt.hist, bins=30) # In[31]: returns_fig = sns.PairGrid(closing_df) returns_fig.map_upper(plt.scatter, color='purple') returns_fig.map_lower(sns.kdeplot, cmap='cool_d') returns_fig.map_diag(plt.hist, bins=30) # In[32]: sns.corrplot(tech_rets.dropna(), annot=True) # In[ ]: #risk analysis # In[36]: # Let's start by defining a new DataFrame as a clenaed version of the oriignal tech_rets DataFrame rets = tech_rets.dropna() area = np.pi * 20 plt.scatter(rets.mean(), rets.std(), alpha=0.5, s=area) # Set the x and y limits of the plot (optional, remove this if you don't see anything in your plot)
''' FacetGrid. ''' # FacetGrid is used to draw plots with multiple Axes where each Axes shows the same relationship conditioned on different levels of some variable
myimg = sns.FacetGrid(dframe,hue = 'Stories', col = 'zone', row = 'homebr') # set the grid
myimg = myimg.map(sns.pointplot, 'pricePerSqft') # set the plot type
myimg = sns.FacetGrid(dframe,hue = 'Stories', row = 'zone', aspect = 4) # set the grid
myimg = myimg.map(sns.kdeplot, 'homeprice', shade = True).add_legend().set_axis_labels("Home Prices") # set the plot type
''' Correlation Visualization ''' # aka correlation plots
sns.pairplot(dlyReturns_df)
dlyReturns_fig = sns.PairGrid(dlyReturns_df, size = 5, aspect = 2)
dlyReturns_fig.map_upper(plt.scatter, color = 'darkblue')
dlyReturns_fig.map_lower(sns.kdeplot, cmap = 'cool_d')
dlyReturns_fig.map_diag(plt.hist, bins = 30)
sns.corrplot(dlyReturns_df, annot = True)
###############################################################
### ###
### ###
### Importing stock prices ###
### ###
### ###
###############################################################
import pandas.io.data as pdweb
import datetime
from pandas.io.data import DataReader
from datetime import datetime
from __future__ import division # dont have to worry about division complications with python 2.7
groupby('Stock').resample('M', how='sum')
#convert back to dataframe
AEV_vol_comb = AEV_vol_comb.reset_index().reindex(columns=['Date','Volume'])
AEV_vol_comb['Month'] = AEV_vol_comb['Date'].dt.month#create col for Month
sns.barplot("Month", y="Volume",data=AEV_vol_comb,
palette="BuGn_d")
#loop through different stocks to compare each other using seaborn
sns.pairplot(myport_rets.dropna())
#correlation bet. closing prices of all stock tickers
returns_fig = sns.PairGrid(myport_Close.dropna())
returns_fig.map_upper(plt.scatter,color='purple')
returns_fig.map_lower(sns.kdeplot,cmap='cool_d')
returns_fig.map_diag(plt.hist,bins=30)
#correlation plot bet. daily returns of all stock tickers
sns.corrplot(myport_rets.dropna(),annot=True)
#sns.jointplot(myport_rets['URC'],myport_rets['CEB'])#joint plot of both datasets
#sns.jointplot(myport_rets['URC'],myport_rets['CEB'],kind='hex')#plot using hex
#sns.jointplot(myport_rets['URC'],myport_rets['JGS'])#joint plot of both datasets
#sns.jointplot(myport_rets['URC'],myport_rets['JGS'],kind='hex')#plot using hex
#sns.jointplot(myport_rets['JGS'],myport_rets['CEB'])#joint plot of both datasets
#sns.jointplot(myport_rets['JGS'],myport_rets['CEB'],kind='hex')#plot using hex
#correlation plot bet. closing prices of all stock tickers
sns.corrplot(myport_Close.dropna(),annot=True)
#######################################
# RISK ANALYSIS
# (A) There are many ways we can quantify risk, one of the most basic ways
# using the info. we've gathered on daily percentage returns is by
# comparing the expected return with the standard deviation of the
data[col] = data[col].astype('float')
# plot the distribution of the predictors using histograms
for col in columns:
fig, ax = plt.subplots()
data[col].hist()
plt.title(col + 'distribution')
plt.savefig(col)
# plot the correlation matrix of the dataset to see if some predictors are more correlated to the response
seaborn.corrplot(data,
sig_stars=True,
annot=False,
sig_tail='both',
sig_corr=False,
cmap=None,
cmap_range=None,
cbar=True,
diag_names=True,
method='spearman',
ax=None)
plt.savefig('correlation_spearman_matrix.png')
# separate the response from the features
y = data['loan_status']
data.drop('loan_status', inplace=True, axis=1)
def Logistic_Regression(X, y, fold):
# shuffle and split training and test sets
gini = 0
# -*- coding: UTF-8 -*-
#numpy科学计算工具箱
import numpy as np
#使用make_classification构造1000个样本，每个样本有20个feature
from sklearn.datasets import make_classification
X, y = make_classification(1000,
n_features=20,
n_informative=2,
n_redundant=2,
n_classes=2,
random_state=0)
#存为dataframe格式
from pandas import DataFrame
df = DataFrame(np.hstack((X, y[:, None])), columns=range(20) + ["class"])
print df[:6]
import matplotlib.pyplot as plt
import seaborn as sns
#使用pairplot去看不同特征维度pair下数据的空间分布状况
_ = sns.pairplot(df[:50], vars=[8, 11, 12, 14, 19], hue="class", size=1.5)
plt.show()
import matplotlib.pyplot as plt
plt.figure(figsize=(12, 10))
_ = sns.corrplot(df, annot=False)
plt.show()
def inspect_correlations(ModelTrains, filedir='data/plots', FIGWIDTH=FIGWIDTH, FIGHEIGHT=FIGHEIGHT):
'''Produce Correlation Matrices with Nonmissing Trainer Objects'''
plt.close('all')
nonissing_trainers = []
for trainer in ModelTrains.trainers:
save_this_directory = filedir + '/{}'.format(trainer.name)
save_this_here = save_this_directory + '/correlations'
try:
os.mkdir(filedir)
except:
pass
try:
os.mkdir(save_this_directory)
except:
pass
try:
os.mkdir(save_this_here)
except:
pass
try:
plt.close('all')
# Compute the correlation matrix
corr = trainer.now.corr()
# Generate a mask for the upper triangle
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
# Set up the matplotlib figure
fig, axs = plt.subplots(figsize=(FIGWIDTH, FIGHEIGHT))
# Generate a custom diverging colormap
cmap = sns.diverging_palette(220, 10, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
# with sns.axes_style("white"):
# g = sns.heatmap(corr, mask=mask, cmap=cmap, cbar_ax=1,
# vmax=.3, square=True, cbar=True,
# cbar_kws={"shrink": .5}, linewidths=.5)
# g = sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3,
# square=True, xticklabels=5, yticklabels=5,
# linewidths=.5, cbar_kws={"shrink": .5},
# legend_out=True, ax=axs)
g = sns.corrplot(trainer.now, annot=False, diag_names=False)
doc = save_this_here + '/matrix_{}_{}.png'.format(trainer.name, trainer.id)
t = '\n{}\n(Anti)Correlation Matrix\n{}'.format(trainer.name,
trainer.shape)
plt.title(t, fontsize=12)
plt.tight_layout()
fig.savefig(doc)
plt.close('all')
except:
pass
if trainer.now.isnull().sum().sum() == 0:
plt.close('all')
# x, y, z, a = trainer.get_attributes()
nonissing_trainers.append(trainer)
fig, axs = plt.subplots(figsize=(FIGWIDTH, FIGWIDTH))
# plt.figure(figsize=())
g = sns.corrplot(trainer.now, annot=False, diag_names=False)
t = "\n{}\nNon-Missing Only Correlation Matrix,\n{}".format(trainer.name,
trainer.shape)
doc = '{}/{}.png'.format(save_this_directory,'corrplot_{}_{}'.format(trainer.name,
trainer.id))
# g.add_legend()
plt.title(t)
plt.tight_layout()
fig.savefig(doc)
plt.close('all')
plt.close('all')
import pandas
import seaborn
import numpy
from matplotlib import pyplot
df = pandas.read_csv("df.csv")
counts = df['hur.count'].copy()
del df['hur.count']
df.columns = [x.replace(".data", "") for x in df.columns]
seaborn.set(style="darkgrid")
rs = numpy.random.RandomState(33)
f, ax = pyplot.subplots(figsize=(9, 9))
cmap = seaborn.diverging_palette(220, 10, as_cmap=True)
seaborn.corrplot(df,
annot=False,
sig_stars=False,
diag_names=False,
cmap=cmap,
ax=ax)
f.tight_layout()
pyplot.savefig("corr.png")
pyplot.close()
pyplot.figure(figsize=(9, 9))
seaborn.distplot(counts)
pyplot.xlim([0, 20])
pyplot.xlabel("Hurricanes")
pyplot.title("Histogram of Annual Hurricane Counts")
pyplot.savefig("histogram.png")
def visualize(training_data, X, y, pca):
"""
Computes statistics describing the data and creates some visualizations
that attempt to highlight the underlying structure.
Note: Use '%matplotlib inline' and '%matplotlib qt' at the IPython console
to switch between display modes.
"""
print('Generating individual feature histograms...')
num_features = len(training_data.columns)
num_plots = num_features / 16 if num_features % 16 == 0 else num_features / 16 + 1
for i in range(num_plots):
fig, ax = plt.subplots(4, 4, figsize=(20, 10))
for j in range(16):
index = (i * 16) + j
if index == 0:
ax[j / 4, j % 4].hist(y, bins=30)
ax[j / 4, j % 4].set_title(training_data.columns[index])
ax[j / 4, j % 4].set_xlim((min(y), max(y)))
elif index < num_features:
ax[j / 4, j % 4].hist(X[:, index - 1], bins=30)
ax[j / 4, j % 4].set_title(training_data.columns[index])
ax[j / 4, j % 4].set_xlim(
(min(X[:, index - 1]), max(X[:, index - 1])))
fig.tight_layout()
print('Generating correlation matrix...')
fig2, ax2 = plt.subplots(figsize=(16, 10))
colormap = sb.blend_palette(
["#00008B", "#6A5ACD", "#F0F8FF", "#FFE6F8", "#C71585", "#8B0000"],
as_cmap=True)
sb.corrplot(training_data,
annot=False,
sig_stars=False,
diag_names=False,
cmap=colormap,
ax=ax2)
fig2.tight_layout()
if pca is not None:
print('Generating principal component plots...')
X = pca.transform(X)
class_count = np.count_nonzero(np.unique(y))
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'w']
fig3, ax3 = plt.subplots(figsize=(16, 10))
for i in range(class_count):
class_idx = i + 1 # add 1 if class labels start at 1 instead of 0
ax3.scatter(X[y == class_idx, 0],
X[y == class_idx, 1],
c=colors[i],
label=class_idx)
ax3.set_title('First & Second Principal Components')
ax3.legend()
fig3.tight_layout()
fig4, ax4 = plt.subplots(figsize=(16, 10))
for i in range(class_count):
class_idx = i + 1 # add 1 if class labels start at 1 instead of 0
ax4.scatter(X[y == class_idx, 1],
X[y == class_idx, 2],
c=colors[i],
label=class_idx)
ax4.set_title('Second & Third Principal Components')
ax4.legend()
fig4.tight_layout()
fig5, ax5 = plt.subplots(figsize=(16, 10))
for i in range(class_count):
class_idx = i + 1 # add 1 if class labels start at 1 instead of 0
ax5.scatter(X[y == class_idx, 2],
X[y == class_idx, 3],
c=colors[i],
label=class_idx)
ax5.set_title('Third & Fourth Principal Components')
ax5.legend()
fig5.tight_layout()
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 9 12:43:14 2015
@author: davekensinger
"""
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="darkgrid")
rs = np.random.RandomState(33)
d = rs.normal(size=(100, 30))
f, ax = plt.subplots(figsize=(9, 9))
cmap = sns.diverging_palette(220, 10, as_cmap=True)
sns.corrplot(d,
annot=False,
sig_stars=False,
diag_names=False,
cmap=cmap,
ax=ax)
f.tight_layout()
df = pd.DataFrame(np.transpose([x, y]), columns=["X", "Y"])
sns.regplot("X", 'Y', df)
sns.regplot("X", 'Y', df, ci=None, color='slategray')
r2 = lambda x, y: stats.pearson(x, y)[0] ** 2
sns.regplot('X', 'Y', df, corr_func=r2, func_name='$R^2$', color='seagreen')
tips = pd.read_csv("https://raw.github.com/mwaskom/seaborn/master/examples/tips.csv")
tips["big_tip"] = tips.tip > (.2 * tips.total_bill)
tips["smoker"] = tips["smoker"] == "Yes"
tips["female"] = tips["sex"] == "Female"
mpl.rc("figure", figsize=(7, 7))
sns.corrplot(tips)
sns.corrplot(tips, sig_stars=False)
sns.corrplot(tips, sig_tail='upper', cmap='PuRd', cmap_range=(-.2, .8))
mpl.rc('figure', figsize=(5, 5))
sns.lmplot('total_bill', 'tip', tips)
sns.lmplot('total_bill', 'tip', tips, color='time')
sns.lmplot('total_bill', 'tip', tips, color='day', palette='muted', ci=None)
tips['tip_sqr'] = tips.tip ** 2
sns.lmplot('total_bill', 'tip_sqr', tips, order=2)
sns.lmplot('size', 'big_tip', tips)
sns.lmplot('size', 'big_tip', tips, x_jitter=0.3, y_jitter=0.075)
sns.lmplot('size', 'big_tip', tips, x_jitter=0.3, y_jitter=0.075, logistic=True, n_boot=1000)
sns.lmplot('total_bill', 'tip', tips, col='sex')
def make_correlation_plot(df):
f, ax = plt.subplots(figsize=(12, 12))
sns.corrplot(df, annot=True, sig_stars=False,
diag_names=False, ax=ax)
