爬取淘宝店铺商品销量，规格等参数及可视化

""" ======================================= Visualizing the stock market structure ======================================= This example employs several unsupervised learning techniques to extract the stock market structure from variations in historical quotes. The quantity that we use is the daily variation in quote price: quotes that are linked tend to cofluctuate during a day. .. _stock_market: Learning a graph structure -------------------------- We use sparse inverse covariance estimation to find which quotes are correlated conditionally on the others. Specifically, sparse inverse covariance gives us a graph, that is a list of connection. For each symbol, the symbols that it is connected too are those useful to explain its fluctuations. Clustering ---------- We use clustering to group together quotes that behave similarly. Here, amongst the :ref:`various clustering techniques <clustering>` available in the scikit-learn, we use :ref:`affinity_propagation` as it does not enforce equal-size clusters, and it can choose automatically the number of clusters from the data. Note that this gives us a different indication than the graph, as the graph reflects conditional relations between variables, while the clustering reflects marginal properties: variables clustered together can be considered as having a similar impact at the level of the full stock market. Embedding in 2D space --------------------- For visualization purposes, we need to lay out the different symbols on a 2D canvas. For this we use :ref:`manifold` techniques to retrieve 2D embedding. Visualization ------------- The output of the 3 models are combined in a 2D graph where nodes represents the stocks and edges the: - cluster labels are used to define the color of the nodes - the sparse covariance model is used to display the strength of the edges - the 2D embedding is used to position the nodes in the plan This example has a fair amount of visualization-related code, as visualization is crucial here to display the graph. One of the challenge is to position the labels minimizing overlap. For this we use an heuristic based on the direction of the nearest neighbor along each axis. """ from __future__ import print_function # Author: Gael Varoquaux gael.varoquaux@normalesup.org # License: BSD 3 clause import sys from datetime import datetime import numpy as np import matplotlib.pyplot as plt from matplotlib.collections import LineCollection import pandas as pd from sklearn import cluster, covariance, manifold print(__doc__) # ############################################################################# # Retrieve the data from Internet # The data is from 2003 - 2008. This is reasonably calm: (not too long ago so # that we get high-tech firms, and before the 2008 crash). This kind of # historical data can be obtained for from APIs like the quandl.com and # alphavantage.co ones. start_date = datetime(2003, 1, 1).date() end_date = datetime(2008, 1, 1).date() symbol_dict = { 'TOT': 'Total', 'XOM': 'Exxon', 'CVX': 'Chevron', 'COP': 'ConocoPhillips', 'VLO': 'Valero Energy', 'MSFT': 'Microsoft', 'IBM': 'IBM', 'TWX': 'Time Warner', 'CMCSA': 'Comcast', 'CVC': 'Cablevision', 'YHOO': 'Yahoo', 'DELL': 'Dell', 'HPQ': 'HP', 'AMZN': 'Amazon', 'TM': 'Toyota', 'CAJ': 'Canon', 'SNE': 'Sony', 'F': 'Ford', 'HMC': 'Honda', 'NAV': 'Navistar', 'NOC': 'Northrop Grumman', 'BA': 'Boeing', 'KO': 'Coca Cola', 'MMM': '3M', 'MCD': 'McDonald\'s', 'PEP': 'Pepsi', 'K': 'Kellogg', 'UN': 'Unilever', 'MAR': 'Marriott', 'PG': 'Procter Gamble', 'CL': 'Colgate-Palmolive', 'GE': 'General Electrics', 'WFC': 'Wells Fargo', 'JPM': 'JPMorgan Chase', 'AIG': 'AIG', 'AXP': 'American express', 'BAC': 'Bank of America', 'GS': 'Goldman Sachs', 'AAPL': 'Apple', 'SAP': 'SAP', 'CSCO': 'Cisco', 'TXN': 'Texas Instruments', 'XRX': 'Xerox', 'WMT': 'Wal-Mart', 'HD': 'Home Depot', 'GSK': 'GlaxoSmithKline', 'PFE': 'Pfizer', 'SNY': 'Sanofi-Aventis', 'NVS': 'Novartis', 'KMB': 'Kimberly-Clark', 'R': 'Ryder', 'GD': 'General Dynamics', 'RTN': 'Raytheon', 'CVS': 'CVS', 'CAT': 'Caterpillar', 'DD': 'DuPont de Nemours'} symbols, names = np.array(sorted(symbol_dict.items())).T quotes = [] for symbol in symbols: print('Fetching quote history for %r' % symbol, file=sys.stderr) url = ('https://raw.githubusercontent.com/scikit-learn/examples-data/' 'master/financial-data/{}.csv') quotes.append(pd.read_csv(url.format(symbol))) close_prices = np.vstack([q['close'] for q in quotes]) open_prices = np.vstack([q['open'] for q in quotes]) # The daily variations of the quotes are what carry most information variation = close_prices - open_prices # ############################################################################# # Learn a graphical structure from the correlations edge_model = covariance.GraphicalLassoCV(cv=5) # standardize the time series: using correlations rather than covariance # is more efficient for structure recovery X = variation.copy().T X /= X.std(axis=0) edge_model.fit(X) # ############################################################################# # Cluster using affinity propagation _, labels = cluster.affinity_propagation(edge_model.covariance_) n_labels = labels.max() for i in range(n_labels + 1): print('Cluster %i: %s' % ((i + 1), ', '.join(names[labels == i]))) # ############################################################################# # Find a low-dimension embedding for visualization: find the best position of # the nodes (the stocks) on a 2D plane # We use a dense eigen_solver to achieve reproducibility (arpack is # initiated with random vectors that we don't control). In addition, we # use a large number of neighbors to capture the large-scale structure. node_position_model = manifold.LocallyLinearEmbedding( n_components=2, eigen_solver='dense', n_neighbors=6) embedding = node_position_model.fit_transform(X.T).T # ############################################################################# # Visualization plt.figure(1, facecolor='w', figsize=(10, 8)) plt.clf() ax = plt.axes([0., 0., 1., 1.]) plt.axis('off') # Display a graph of the partial correlations partial_correlations = edge_model.precision_.copy() d = 1 / np.sqrt(np.diag(partial_correlations)) partial_correlations *= d partial_correlations *= d[:, np.newaxis] non_zero = (np.abs(np.triu(partial_correlations, k=1)) > 0.02) # Plot the nodes using the coordinates of our embedding plt.scatter(embedding[0], embedding[1], s=100 * d ** 2, c=labels, cmap=plt.cm.nipy_spectral) # Plot the edges start_idx, end_idx = np.where(non_zero) # a sequence of (*line0*, *line1*, *line2*), where:: # linen = (x0, y0), (x1, y1), ... (xm, ym) segments = [[embedding[:, start], embedding[:, stop]] for start, stop in zip(start_idx, end_idx)] values = np.abs(partial_correlations[non_zero]) lc = LineCollection(segments, zorder=0, cmap=plt.cm.hot_r, norm=plt.Normalize(0, .7 * values.max())) lc.set_array(values) lc.set_linewidths(15 * values) ax.add_collection(lc) # Add a label to each node. The challenge here is that we want to # position the labels to avoid overlap with other labels for index, (name, label, (x, y)) in enumerate( zip(names, labels, embedding.T)): dx = x - embedding[0] dx[index] = 1 dy = y - embedding[1] dy[index] = 1 this_dx = dx[np.argmin(np.abs(dy))] this_dy = dy[np.argmin(np.abs(dx))] if this_dx > 0: horizontalalignment = 'left' x = x + .002 else: horizontalalignment = 'right' x = x - .002 if this_dy > 0: verticalalignment = 'bottom' y = y + .002 else: verticalalignment = 'top' y = y - .002 plt.text(x, y, na