joseph-allen

# import packages
import matplotlib as plt
import seaborn as sns
import pandas as pd

# set seaborn style
sns.set(rc={'figure.figsize':(12,8)})
sns.set(font_scale=1.5)

# create UKDS color palette

joseph-allen

import pandas as pd
import numpy as np

from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import  mean_absolute_error

def train_polynomial(df, train_upto,train_degree):
    # build Polynomial features up to degree train_degree 
    p = PolynomialFeatures(degree=train_degree).fit(df[['dv','psi','temp1','temp2']])

joseph-allen

import pickle
import pandas as pd
import numpy as np

# Create sample data
df = pd.DataFrame(columns=['A','B','C'])
df.loc[0] = [12,42,'test']

# load stored model
loaded_model = pickle.load(open('Pickled_Model.pkl', 'rb'))

joseph-allen

from sklearn.preprocessing import PolynomialFeatures

p = PolynomialFeatures(degree=2).fit(df[['feature1','feature2]])
features = pd.DataFrame(p.transform(df[['feature1','feature2]]), columns=p.get_feature_names(df[['feature1','feature2]].columns))

features.head()

joseph-allen

import glob

path = r'path to file' # use your path
all_files = glob.glob(path + "/*.csv")

li = []

for filename in all_files:
    df = pd.read_csv(filename, index_col=None, header=0,sep=',|;')
    print(filename)

joseph-allen

def plot_multi(data, cols=None, spacing=.1, **kwargs):

    from pandas import plotting

    # Get default color style from pandas - can be changed to any other color list
    if cols is None: cols = data.columns
    if len(cols) == 0: return
    colors = getattr(getattr(plotting, '_style'), '_get_standard_colors')(num_colors=len(cols))

    # First axis

joseph-allen

import plotly as py
py.tools.set_credentials_file(username='YOURE_USERNAME', api_key='YOUR API KEY')
import cufflinks as cf
import pandas as pd

df = pd.read_csv('data.csv')

py.plotly.iplot([{
    'x': df.var0,
    'y': df[col],

joseph-allen

import pandas as pd

# Visualisation
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.dates as mdates

# Configure visualisations
%matplotlib inline
mpl.style.use( 'ggplot' )

joseph-allen

## Random Forest params
rf_param_grid = {"max_depth": [None],
              "max_features": [1, 3],
              "min_samples_split": [2, 3],
              "min_samples_leaf": [1, 3],
              "bootstrap": [False],
              "n_estimators" :[100],
              "criterion": ["gini"]}

## Extra Trees params

joseph-allen

import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import GridSearchCV, StratifiedKFold, learning_curve
from sklearn.ensemble import GradientBoostingClassifier


def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
                        n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 5)):
    """Generate a simple plot of the test and training learning curve"""
    plt.figure()