Python sklearn.preprocessing.minmax_scale() Examples

The following are 12 code examples for showing how to use sklearn.preprocessing.minmax_scale(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

You may check out the related API usage on the sidebar.

You may also want to check out all available functions/classes of the module sklearn.preprocessing , or try the search function .

Example 1
Project: deepJDOT   Author: bbdamodaran   File: preprocess.py    License: MIT License 6 votes vote down vote up
def min_max_scaling(data, lowerbound_zero=False):
    from sklearn.preprocessing import minmax_scale
    size = data.shape
    data = data/255.0
    if not lowerbound_zero:
        data = (data *2.0)-1.0
    data[np.isnan(data)] = 0
    # if (len(size)==4):
    #     for i in range(size[3]):
    #         tmp = minmax_scale(data[:,:,:,i].reshape(-1, size[1]*size[2]),
    #                            feature_range = (s, t), axis=1)
    #         data[:,:,:,i] = tmp.reshape(-1,size[1],size[2])
    # elif (len(size)==3):
    #    data = minmax_scale(data.reshape(-1, size[1]*size[2]), axis=1)
    #    data = data.reshape(-1, size[1],size[2])


    return data 
Example 2
Project: BrainSpace   Author: MICA-MNI   File: plot_tutorial2.py    License: BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def fusion(*args):
    from scipy.stats import rankdata
    from sklearn.preprocessing import minmax_scale

    max_rk = [None] * len(args)
    masks = [None] * len(args)
    for j, a in enumerate(args):
        m = masks[j] = a != 0
        a[m] = rankdata(a[m])
        max_rk[j] = a[m].max()

    max_rk = min(max_rk)
    for j, a in enumerate(args):
        m = masks[j]
        a[m] = minmax_scale(a[m], feature_range=(1, max_rk))

    return np.hstack(args)


# fuse the matrices 
Example 3
Project: BrainSpace   Author: MICA-MNI   File: plot_tutorial2.py    License: BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def fusion(*args):
    from scipy.stats import rankdata
    from sklearn.preprocessing import minmax_scale

    max_rk = [None] * len(args)
    masks = [None] * len(args)
    for j, a in enumerate(args):
        m = masks[j] = a != 0
        a[m] = rankdata(a[m])
        max_rk[j] = a[m].max()

    max_rk = min(max_rk)
    for j, a in enumerate(args):
        m = masks[j]
        a[m] = minmax_scale(a[m], feature_range=(1, max_rk))

    return np.hstack(args)


# fuse the matrices 
Example 4
Project: gempy   Author: cgre-aachen   File: coKriging.py    License: GNU Lesser General Public License v3.0 6 votes vote down vote up
def preprocess(self):
        """
        Normalization of data between 0 and 1 and subtraction of the nuggets
        Returns:
            pandas.core.frame.DataFrame: Dataframe containing the transformed data
            pandas.core.frame.DataFrame: Containing the substracted nuggets

        """
        import sklearn.preprocessing as skp

        # Normalization
        scaled_data = pn.DataFrame(skp.minmax_scale(self.exp_var_raw[self.properties]), columns=self.properties)

        # Nuggets
        nuggets = scaled_data[self.properties].iloc[0]
        processed_data = scaled_data - nuggets
        return processed_data, nuggets 
Example 5
Project: Quora   Author: KevinLiao159   File: model_v40_BAK.py    License: MIT License 5 votes vote down vote up
def features_transformer(df_text):
    from nlp import meta_features_transformer
    from nlp import topic_features_transformer
    # get features
    meta_features = meta_features_transformer(df_text).values
    topic_features = topic_features_transformer(df_text).values
    # concat
    joined_features = np.hstack([meta_features, topic_features])
    return minmax_scale(joined_features) 
Example 6
Project: CausalDiscoveryToolbox   Author: FenTechSolutions   File: RECI.py    License: MIT License 5 votes vote down vote up
def b_fit_score(self, x, y):
        """ Compute the RECI fit score

        Args:
            x (numpy.ndarray): Variable 1
            y (numpy.ndarray): Variable 2

        Returns:
            float: RECI fit score

        """
        x = np.reshape(minmax_scale(x), (-1, 1))
        y = np.reshape(minmax_scale(y), (-1, 1))
        poly = PolynomialFeatures(degree=self.degree)
        poly_x = poly.fit_transform(x)

        poly_x[:,1] = 0
        poly_x[:,2] = 0

        regressor = LinearRegression()
        regressor.fit(poly_x, y)

        y_predict = regressor.predict(poly_x)
        error = mean_squared_error(y_predict, y)

        return error 
Example 7
Project: HoloScope   Author: shenghua-liu   File: holoscopeFraudDect.py    License: Apache License 2.0 5 votes vote down vote up
def weightWithDropslop(self, weighted, scale):
        'weight the adjacency matrix with the sudden drop of ts for each col'
        if weighted:
            colWeights = np.multiply(self.tspim.dropslops, self.tspim.dropfalls)
        else:
            colWeights = self.tspim.dropslops
        if scale == 'logistic':
            from scipy.stats import logistic
            from sklearn import preprocessing
            'zero mean scale'
            colWeights = preprocessing.scale(colWeights)
            colWeights = logistic.cdf(colWeights)
        elif scale == 'linear':
            from sklearn import preprocessing
            #add a base of suspecious for each edge
            colWeights = preprocessing.minmax_scale(colWeights) +1
        elif scale == 'plusone':
            colWeights += 1
        elif scale == 'log1p':
            colWeights = np.log1p(colWeights) + 1
        else:
            print '[Warning] no scale for the prior weight'

        n = self.nV
        colDiag = lil_matrix((n, n))
        colDiag.setdiag(colWeights)
        self.graphr = self.graphr * colDiag.tocsr()
        self.graph = self.graphr.tocoo(copy=False)
        self.graphc = self.graph.tocsc(copy=False)
        print "finished computing weight matrix" 
Example 8
Project: HoloScope   Author: shenghua-liu   File: holoscopeFraudDect.py    License: Apache License 2.0 5 votes vote down vote up
def evalsusp4rate(self, suspusers, neutral=False, scale='max'):
        susprates = self.ratepim.suspratedivergence(neutral, delta=True)
        if scale == 'max':
            assert(self.ratepim.maxratediv > 0)
            nsusprates = susprates/self.ratepim.maxratediv
        elif scale=='minmax':
            #need a copy, and do not change susprates' value for delta
            nsusprates = preprocessing.minmax_scale(susprates, copy=True)
        else:
            #no scale 
            nsusprates = susprates
        return nsusprates 
Example 9
Project: pylinac   Author: jrkerns   File: tools.py    License: MIT License 5 votes vote down vote up
def process_image(path):
    """Load and resize the images and return as flattened numpy array"""
    img = image.load(path, dtype=np.float32)
    resized_img = imresize(img.array, size=(100, 100), mode='F').flatten()
    rescaled_img = preprocessing.minmax_scale(resized_img)
    return rescaled_img 
Example 10
Project: XenonPy   Author: yoshida-lab   File: heatmap.py    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def _transform(self, series):
        series_ = series
        if series.min() != series.max():
            if self.bc:
                with np.errstate(all='raise'):
                    shift = 1e-10
                    tmp = series - series.min() + shift
                    try:
                        series_, _ = boxcox(tmp)
                    except FloatingPointError:
                        series_ = series
        series_ = minmax_scale(series_)
        return series_ 
Example 11
Project: ml_code   Author: zlxy9892   File: img_proc.py    License: Apache License 2.0 5 votes vote down vote up
def getImgAsMatFromFile(filename, width=28, height=28, scale_min=0, scale_max=1):
    #img = io.imread(filename, as_grey=True)
    img = Image.open(filename)
    img = img.resize((width, height), Image.BILINEAR)
    imgArr_2d = np.array(img.convert('L'))
    imgArr_2d = np.float64(1 - imgArr_2d)
    shape_2d = imgArr_2d.shape
    imgArr_1d_scale = preprocessing.minmax_scale(imgArr_2d.flatten(), feature_range=(0, 1))
    return imgArr_1d_scale.reshape(shape_2d) 
Example 12
Project: python-urbanPlanning   Author: richieBao   File: vectorSpatialAnalysis.py    License: MIT License 4 votes vote down vote up
def G_display(G):
    # make new graph
    H = nx.Graph()
    for v in G:
        # print(v)
        H.add_node(v)
    weightValue=list(nx.get_edge_attributes(G,'weight').values()) #提取权重
    # weightsForWidth=[G[u][v]['weight'] for u,v in G.edges()] #another way
    # print(weightValue)
    import pysal.viz.mapclassify as mc
    q=mc.Quantiles(weightValue,k=30).bins #计算分位数,用于显示值的提取
    # print(q)
  
    for (u, v, d) in tqdm(G.edges(data=True)):
        # print(u,v,d)
        # print()
        # print(d['weight'])
        if d['weight'] > q[28]:
            H.add_edge(u, v)

    print("H_digraph has %d nodes with %d edges"% (nx.number_of_nodes(H), nx.number_of_edges(H)))
    # draw with matplotlib/pylab
    plt.figure(figsize=(18, 18))
    # m=2
    # fig = figure(figsize=(9*m,9*m)
    # with nodes colored by degree sized by value elected
    node_color = [float(H.degree(v)) for v in H]
    # print(node_color)
    # nx.draw(H, G.position,node_size=[G.perimeter[v] for v in H],node_color=node_color, with_labels=True)
    
    weightsForWidthScale=np.interp(weightValue, (min(weightValue), max(weightValue)), (1, 3000)) #setting the edge width
    scaleNode=1
    
    # sklearn.preprocessing.minmax_scale(X, feature_range=(0, 1), axis=0, copy=True)
    nx.draw(H, G.position,node_size=minmax_scale([G.shape_area[v]*scaleNode for v in H],feature_range=(10, 2200)), node_color=node_color,with_labels=True,edge_cmap=plt.cm.Blues,width=weightsForWidthScale) #edge_cmap=plt.cm.Blues
    # scale the axes equally
    # plt.xlim(-5000, 500)
    # plt.ylim(-2000, 3500)

    plt.show()


#CSV文件转.shp格式,并返回关键信息。使用geopandas库实现