Fastbook_Julia/utils.py

# Numpy and pandas by default assume a narrow screen - this fixes that
from fastai.vision.all import *
from nbdev.showdoc import *
from ipywidgets import widgets
from pandas.api.types import CategoricalDtype

import matplotlib as mpl
# mpl.rcParams['figure.dpi']= 200
mpl.rcParams['savefig.dpi']= 200
mpl.rcParams['font.size']=12

set_seed(42)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
pd.set_option('display.max_columns',999)
np.set_printoptions(linewidth=200)
torch.set_printoptions(linewidth=200)

import graphviz
def gv(s): return graphviz.Source('digraph G{ rankdir="LR"' + s + '; }')

def get_image_files_sorted(path, recurse=True, folders=None): return get_image_files(path, recurse, folders).sorted()


# +
# pip install azure-cognitiveservices-search-imagesearch

from azure.cognitiveservices.search.imagesearch import ImageSearchClient as api
from msrest.authentication import CognitiveServicesCredentials as auth

def search_images_bing(key, term, min_sz=128, max_images=150):    
     params = {'q':term, 'count':max_images, 'min_height':min_sz, 'min_width':min_sz}
     headers = {"Ocp-Apim-Subscription-Key":key}
     search_url = "https://api.bing.microsoft.com/v7.0/images/search"
     response = requests.get(search_url, headers=headers, params=params)
     response.raise_for_status()
     search_results = response.json()    
     return L(search_results['value'])


# -

def plot_function(f, tx=None, ty=None, title=None, min=-2, max=2, figsize=(6,4)):
    x = torch.linspace(min,max)
    fig,ax = plt.subplots(figsize=figsize)
    ax.plot(x,f(x))
    if tx is not None: ax.set_xlabel(tx)
    if ty is not None: ax.set_ylabel(ty)
    if title is not None: ax.set_title(title)

# +
from sklearn.tree import export_graphviz

def draw_tree(t, df, size=10, ratio=0.6, precision=0, **kwargs):
    s=export_graphviz(t, out_file=None, feature_names=df.columns, filled=True, rounded=True,
                      special_characters=True, rotate=False, precision=precision, **kwargs)
    return graphviz.Source(re.sub('Tree {', f'Tree {{ size={size}; ratio={ratio}', s))


# +
from scipy.cluster import hierarchy as hc

def cluster_columns(df, figsize=(10,6), font_size=12):
    corr = np.round(scipy.stats.spearmanr(df).correlation, 4)
    corr_condensed = hc.distance.squareform(1-corr)
    z = hc.linkage(corr_condensed, method='average')
    fig = plt.figure(figsize=figsize)
    hc.dendrogram(z, labels=df.columns, orientation='left', leaf_font_size=font_size)
    plt.show()