cli autocomplete and defered imports

This reverts commit bd53a83058.

.gitignore

@@ -2,5 +2,6 @@ __pycache__
 *.html
 .venv
 neuralWeights.json
-neuralWeights.json.bak
+neuralWeights.json.*
 .imgLinkCache.json
+.idea

README.md

@@ -1,4 +1,4 @@
 # CaliGraph
 CaliGraph connects to the database of your local Calibre-Instance in order to recommend which unread books are likely to be enjoyed. The recommendations are displayed in a graph which explains the recommendation by showing correlations to previously read books, authors, recommendations by same individuals, tags...
 
-![Screenshot](https://gitea.dominik.roth.ml/dodox/CaliGraph/raw/branch/master/Screenshot_README.png)
+![Screenshot](./Screenshot_README.png)

py/gp.py

@@ -1,75 +0,0 @@
-import numpy as np
-
-from node2vec import Node2Vec
-from sklearn.gaussian_process.kernels import Kernel, Hyperparameter
-from sklearn.gaussian_process.kernels import GenericKernelMixin
-from sklearn.gaussian_process import GaussianProcessRegressor
-#from sklearn.gaussian_process import GaussianProcessClassifier
-from sklearn.base import clone
-
-class BookKernel(GenericKernelMixin, Kernel):
-    def __init__(self, wv):
-        self.wv = wv
-
-    def _f(self, s1, s2):
-        """
-        kernel value between a pair of sequences
-        """
-        s = self.wv.similarity(s1, s2)**2*0.99 + 0.01
-        if s <= 0:
-            print('bad!')
-        return s
-
-    def __call__(self, X, Y=None, eval_gradient=False):
-        if Y is None:
-            Y = X
-
-        if eval_gradient:
-            return (
-                np.array([[self._f(x, y) for y in Y] for x in X])
-            )
-        else:
-            return np.array([[self._f(x, y) for y in Y] for x in X])
-            #return np.array(self.wv.n_similarity(X, Y))
-
-    def diag(self, X):
-        return self(X)
-
-    def is_stationary(self):
-        return False
-
-    def clone_with_theta(self, theta):
-        cloned = clone(self)
-        cloned.theta = theta
-        return cloned
-
-def genGprScores(G, scoreName='gpr_score', stdName='gpr_std'):
-    print('[\] Constructing Feature-Space-Projector')
-    node2vec = Node2Vec(G, dimensions=32, walk_length=16, num_walks=128, workers=8)
-    print('[\] Fitting Embeddings for Kernel')
-    model = node2vec.fit(window=8, min_count=1, batch_words=4)
-    wv = model.wv
-    print('[\] Constructing Kernel')
-    kernel = BookKernel(wv)
-    print('[\] Fitting GP')
-    X, y = [], []
-    for n in G.nodes:
-        node = G.nodes[n]
-        if 'rating' in node and node['rating']!=None:
-            X.append(n)
-            y.append(node['rating'])
-    gpr = GaussianProcessRegressor(kernel=kernel, random_state=3141, alpha=1e-8).fit(X, y)
-    print('[\] Inferencing GP')
-    X = []
-    for n in G.nodes:
-        node = G.nodes[n]
-        if not 'rating' in node or node['rating']==None:
-            X.append(n)
-    y, stds = gpr.predict(X, return_std=True)
-    i=0
-    for n in G.nodes:
-        node = G.nodes[n]
-        if not 'rating' in node or node['rating']==None:
-            s, std = y[i], stds[i][i][0]
-            i+=1
-            node[scoreName], node[stdName] = float(s), float(std)

requirements.txt

@@ -6,3 +6,9 @@ pyvis
 fuzzywuzzy
 rake_nltk
 ptpython
+requests
+pandas
+plotly
+wikipedia
+argcomplete
+pyzshcomplete