我有一个多项式回归图表，并且试图使用下一个X值来查找预测的y值。
import numpy as npimport matplotlib.pyplot as pltfrom sklearn.linear_model import LinearRegressionimport jsonimport matplotlib.pyplot as pltfrom sklearn.pipeline import make_pipelinefrom sklearn.preprocessing import PolynomialFeatureswith open('/Users/aus10/Desktop/PGA/Data_Cleanup/Combined_Player_Stats.json') as json_file: players_data = json.load(json_file)for obj in players_data: obj['Scrambling_List'] = [i for i in obj['Scrambling_List'] if i]for obj in players_data: def create_2d_lst(lst): try: if len(lst) < 1: return [0, 0] else: return [[i, j] for i, j in enumerate(lst)] except: pass try: scrambling = create_2d_lst(obj['Scrambling_List']) total_putts_GIR = create_2d_lst(obj['Total_Putts_GIR_List']) SG_Putting = create_2d_lst(obj['SG_Putting_List']) except Exception: pass data = scrambling X = np.array(data)[:,0].reshape(-1,1) y = np.array(data)[:,1].reshape(-1,1) poly_reg = PolynomialFeatures(degree=4) X_poly = poly_reg.fit_transform(X) pol_reg = LinearRegression() pol_reg.fit(X_poly, y) predicted_y = poly_reg.fit_transform(X) m = pol_reg.coef_ c = pol_reg.intercept_ prediction_value = (len(X) + 1) prediction = pol_reg.predict(poly_reg.fit_transform([[prediction_value]])) def viz_polymonial(): plt.scatter(X, y, color='red') plt.plot(X, pol_reg.predict(poly_reg.fit_transform(X)), color='blue') plt.plot(prediction, marker='x', color='green') plt.title('Projected Scrambling Percentage') plt.xlabel('Tournaments') plt.ylabel('Scrambling Percentage') plt.show() return viz_polymonial() print(obj['Name'], prediction)当我使用prediction = prediction_value = (len(X) + 1)，prediction = pol_reg.predict(poly_reg.fit_transform([[prediction_value]]))时，我应该得到X的下一个值，但它返回了0，而应该返回len(X) + 1。我需要将X的正确值设置为预测值。我不确定为什么是0，因为当我打印预测值时，我得到了正确的值
这是json文件的副本
[ { "Name": "Aaron Baddeley", "Tournaments": [ { "Scrambling": 71.43, "Total_Putts_GIR": 75, "SG_Putting": 0.31, "Tournament": "Safeway_Open", "Date": "08-26-2019" }, { "Scrambling": 55.56, "Total_Putts_GIR": 92, "SG_Putting": 0.03, "Tournament": "Shriners_Hospital_for_Children_Open", "Date": "10-08-2019" }, { "Scrambling": 40, "Total_Putts_GIR": 47, "SG_Putting": -0.14, "Tournament": "Houston", "Date": "10-10-2019" }, { "Scrambling": 71.43, "Total_Putts_GIR": 93, "SG_Putting": -0.37, "Tournament": "Waste_Management", "Date": "01-30-2020" }, { "Scrambling": 75, "Total_Putts_GIR": 29, "SG_Putting": 0.69, "Tournament": "The_Genesis", "Date": "02-13-2020" }, { "Scrambling": 71.43, "Total_Putts_GIR": 38, "SG_Putting": -0.82, "Tournament": "RBC_Heritage", "Date": "06-18-2020" }, { "Scrambling": 50, "Total_Putts_GIR": 30, "SG_Putting": 0.88, "Tournament": "Travelers", "Date": "06-25-2020" }, { "Scrambling": 42.86, "Total_Putts_GIR": 53, "SG_Putting": -1.18, "Tournament": "Rocket_Mortgage", "Date": "07-02-2020" }, { "Scrambling": 43.75, "Total_Putts_GIR": 33, "SG_Putting": 1.41, "Tournament": "Workday", "Date": "07-09-2020" } ], "Scrambling_List": [ 71.43, 55.56, 40, 71.43, 75, 71.43, 50, 42.86, 43.75 ], "Total_Putts_GIR_List": [ 75, 92, 47, 93, 29, 38, 30, 53, 33 ], "SG_Putting_List": [ 0.31, 0.03, -0.14, -0.37, 0.69, -0.82, 0.88, -1.18, 1.41 ] }]回答：
已经解决了。图表中没有X值，所以它自动设置为0。
import numpy as npimport matplotlib.pyplot as pltfrom sklearn.linear_model import LinearRegressionimport jsonimport matplotlib.pyplot as pltfrom sklearn.pipeline import make_pipelinefrom sklearn.preprocessing import PolynomialFeatureswith open('/Users/aus10/Desktop/PGA/Data_Cleanup/Combined_Player_Stats.json') as json_file: players_data = json.load(json_file)for obj in players_data: obj['Scrambling_List'] = [i for i in obj['Scrambling_List'] if i]for obj in players_data: def create_2d_lst(lst): try: if len(lst) < 1: return [0, 0] else: return [[i, j] for i, j in enumerate(lst)] except: pass try: scrambling = create_2d_lst(obj['Scrambling_List']) total_putts_GIR = create_2d_lst(obj['Total_Putts_GIR_List']) SG_Putting = create_2d_lst(obj['SG_Putting_List']) except Exception: pass data = scrambling X = np.array(data)[:,0].reshape(-1,1) y = np.array(data)[:,1].reshape(-1,1) poly_reg = PolynomialFeatures(degree=4) X_poly = poly_reg.fit_transform(X) pol_reg = LinearRegression() pol_reg.fit(X_poly, y) predicted_y = poly_reg.fit_transform(X) m = pol_reg.coef_ c = pol_reg.intercept_ prediction = pol_reg.predict(poly_reg.fit_transform([[len(X)+1]])) def viz_polymonial(): plt.scatter(X, y, color='red') plt.plot(X, pol_reg.predict(poly_reg.fit_transform(X)), color='blue') plt.plot(len(X)+1, pol_reg.predict(poly_reg.fit_transform([[len(X)+1]])), marker='x', color='green') plt.title('Projected Scrambling Percentage') plt.xlabel('Tournaments') plt.ylabel('Scrambling Percentage') plt.show() return viz_polymonial() print(obj['Name'], prediction)