def show_history(history):
sns.set_style("darkgrid")
sns.set_context("notebook", font_scale=1.5, rc={"lines.linewidth": 1.6})
sns.mpl.rc("figure", figsize=(16, 5))
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.plot(history.losses, color="#47d024")
ax1.plot(history.accuracy, color="#0672b6")
ax1.legend(["loss", "accuracy"])
ax1.set_title("Batch validation")
ax2.plot(history.val_losses, color="#47d024")
ax2.plot(history.val_accuracy, color="#0672b6")
ax2.legend(["loss", "accuracy"])
ax2.set_title("Epoch validation")
plt.show()
@copyright CL all rights reserved
@created Thu Dec 23 2018 15:10 GMT-0800 (PST)
@last-modified Wed Feb 27 2019 19:20 GMT-0800 (PST)
.:.:.::.:.:.:.:.::.:.:.:.:.::.:.:.:.:.::.:.:.:.:.::.:.:'''
from tabulate import tabulate
import matplotlib
import os
import datetime
from matplotlib import pyplot as plt
from mpl_finance import candlestick_ohlc
import matplotlib.dates as mdates
import numpy as np
import seaborn.apionly as sns
sns.set_style("white")
sns.set_context("poster")
import pandas as pd
import mpld3
class VisualChart:
'''
plot visualization
'''
def price_volumn(self, kline_data, save_figure):
'''
price_volumn: plot price and volumn
Param:
kline_data (obj, pandas_dataframe)
'''
fig_file = os.path.join(
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
from matplotlib.ticker import Formatter
from matplotlib import patheffects as PathEffects
from scipy.spatial import Delaunay
#import seaborn as sns
import seaborn.apionly as sns
sns.set_style('ticks')
sns.set_context('talk')
#sns.set_style({'font.family':'Times New Roman'})
rc_params = {
'backend': 'ps',
'axes.labelsize': 15,
'axes.titlesize': 15,
'font.size': 13,
'legend.fontsize': 15,
'xtick.labelsize': 12,
'ytick.labelsize': 12,
#'text.usetex': True,
'font.family': 'Times New Roman'
} #,
#'font.sans-serif': ['Bitstream Vera Sans']}#,
# wherever the files are located
# - there should be a pics subdirectory for picture generation
homepath = '/home/gswarrin/research/gerrymander/'
##################################################
# imports
import pystan
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import seaborn.apionly as sns
sns.set_context('notebook')
from matplotlib.colors import LinearSegmentedColormap
import scipy.stats as stats
# Index of state in this list corresponds to the convention many of our data files use.
# '00' is added at he beginning to make this correspondence correct.
stlist = ['00','AL','AK','AZ','AR','CA','CO','CT','DE','FL','GA','HI','ID','IL','IN','IA','KS','KY',\
'LA','ME','MD','MA','MI','MN','MS','MO','MT','NE','NV','NH','NJ','NM','NY','NC','ND','OH',\
'OK','OR','PA','RI','SC','SD','TN','TX','UT','VT','VA','WA','WV','WI','WY']
GLOBAL_MIN_YEAR = 1971
##################################################
load(homepath + 'util.py')
load(homepath +
'metrics.py') # functions for computing metrics (e.g., declination, EG)
load(homepath + 'read_data.py') # functions for reading in all data
load(homepath + 'model.py') # statistical model for imputing values
load(homepath + 'classes.py') # various classes for storing information
import pandas as pd
import ConfigParser
import lab
import lab.classes.exceptions as exc
import lab.plotting as plotting
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
import seaborn.apionly as sns
sns.set_style("ticks")
sns.set_context(
rc={
'lines.linewidth': 1,
'axes.titlesize': 7,
'axes.labelsize': 'medium',
'xtick.labelsize': 'medium',
'ytick.labelsize': 'medium'
})
import matplotlib as mpl
# mpl.rcParams['font.sans-serif'].append(u'DejaVu Sans')
mpl.rcParams.update({
'lines.linewidth': 1,
'axes.titlesize': 9,
'axes.labelsize': 9,
'xtick.labelsize': 7,
'ytick.labelsize': 7,
'mathtext.fontset': 'dejavuserif',
'font.size': 9,
'pdf.fonttype': 42,
def plot_pca_pairs(clf_pca,
x_train,
y=None,
n_components=3,
diag='kde',
cmap=None,
figsize=(10, 10)):
"""
Create pairwise plots of principal components from x data.
Colors the components according to the `y` values.
Parameters
----------
clf_pca : sklearn.decomposition.PCA
A fitted scikit-learn PCA model.
x_train : numpy.ndarray
Training data used to fit `clf_pca`, either scaled or un-scaled,
depending on how `clf_pca` was fit.
y : numpy.ndarray
Target training values, of shape = [n_samples].
n_components: int
Desired number of principal components to plot. Defaults to 3.
diag : str
Type of plot to display on the diagonals. Default is 'kde'.
* 'kde': density curves
* 'hist': histograms
cmap : str
A string representation of a Seaborn color map. See available maps:
https://stanford.edu/~mwaskom/software/seaborn/tutorial/color_palettes.
figsize : tuple
A tuple indicating the size of the plot to be created, with format
(x-axis, y-axis). Defaults to (10, 10).
Returns
-------
matplotlib.figure.Figure
The Figure instance.
"""
if y is not None:
assert y.shape[0] == x_train.shape[0], (
"Dimensions of y {0} do not match dimensions of x_train {1}".
format(y.shape[0], x_train.shape[0]))
# Obtain the projections of x_train
x_projection = clf_pca.transform(x_train)
# Create a data frame to hold the projections of n_components PCs
col_names = ["PC{0}".format(i + 1) for i in range(n_components)]
df = pd.DataFrame(x_projection[:, 0:n_components], columns=col_names)
# Generate the plot
cmap = "Greys" if cmap is None else cmap
color = "#55A969" if y is None else y
sns.set_style("white", {"axes.linewidth": "0.8", "image.cmap": cmap})
sns.set_context("notebook")
try:
# Create figure instance with subplot and populate the subplot with
# the scatter matrix. You need to do this so you can access the figure
# properties later to increase distance between subplots. If you don't,
# Pandas will create its own figure with a tight layout.
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(1, 1, 1)
from pandas.tools.plotting import scatter_matrix
axes = scatter_matrix(df,
ax=ax,
alpha=0.7,
figsize=figsize,
diagonal=diag,
marker='o',
c=color,
density_kwds={'c': '#6283B9'},
hist_kwds={
'facecolor': '#5A76A4',
'edgecolor': '#3D3D3D'
})
# Increase space between subplots
fig.subplots_adjust(hspace=0.1, wspace=0.1)
# Loop through subplots and remove top and right axes
axes_unwound = np.ravel(axes)
for i in range(axes_unwound.shape[0]):
ax = axes_unwound[i]
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.show()
except:
raise # Re-raise the exception
else:
sns.reset_orig()
return fig
finally:
sns.reset_orig()
def plot_residuals(clf, X, y, r_type='standardized', figsize=(10, 8)):
"""Plot residuals of a linear model.
Parameters
----------
clf : sklearn.linear_model
A scikit-learn linear model classifier with a `predict()` method.
X : numpy.ndarray
Training data used to fit the classifier.
y : numpy.ndarray
Target training values, of shape = [n_samples].
r_type : str
Type of residuals to return: 'raw', 'standardized', 'studentized'.
Defaults to 'standardized'.
* 'raw' will return the raw residuals.
* 'standardized' will return the standardized residuals, also known as
internally studentized residuals, which is calculated as the residuals
divided by the square root of MSE (or the STD of the residuals).
* 'studentized' will return the externally studentized residuals, which
is calculated as the raw residuals divided by sqrt(LOO-MSE * (1 -
leverage_score)).
figsize : tuple
A tuple indicating the size of the plot to be created, with format
(x-axis, y-axis). Defaults to (10, 8).
Returns
-------
matplotlib.figure.Figure
The Figure instance.
"""
# Ensure we only plot residuals using classifiers we have tested
assert isinstance(clf, _utils.supported_linear_models), (
"Classifiers of type {0} not currently supported.".format(type(clf)))
# Get residuals or standardized residuals
resids = stats.residuals(clf, X, y, r_type)
predictions = clf.predict(X)
# Prepare plot labels to use, depending on which type of residuals used
y_label = {
'raw': 'Residuals',
'standardized': 'Standardized Residuals',
'studentized': 'Studentized Residuals'
}
# Set plot style
sns.set_style("whitegrid")
sns.set_context("talk") # Increase font size on plot
# Generate residual plot
try:
fig = plt.figure('residuals', figsize=figsize)
plt.scatter(predictions, resids, s=14, c='gray', alpha=0.7)
plt.hlines(y=0,
xmin=predictions.min(),
xmax=predictions.max(),
linestyle='dotted')
plt.title("Residuals Plot")
plt.xlabel("Predictions")
plt.ylabel(y_label[r_type])
plt.show()
except:
raise # Re-raise the exception
finally:
sns.reset_orig() # Always reset back to default matplotlib styles
return fig