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analysis.py - v 1.0.6.002
changelog: - bug fixes
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ltcptgeneral
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@ -8,9 +8,12 @@
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#setup:
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__version__ = "1.0.6.001"
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__version__ = "1.0.6.002"
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#changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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1.0.6.002:
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- bug fixes
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1.0.6.001:
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- corrected __all__ to contain all of the functions
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1.0.6.000:
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@ -62,7 +65,7 @@ __changelog__ = """changelog:
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- major bug fixes
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1.0.0.xxx:
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- added loading csv
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- added 1d, column, row basic stats""" #changelog should be viewed using print(analysis.__changelog__)
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- added 1d, column, row basic stats"""
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__author__ = (
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"Arthur Lu <arthurlu@ttic.edu>, "
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@ -392,7 +395,6 @@ def basic_stats(data, method, arg): # data=array, mode = ['1d':1d_basic_stats, '
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data_t = []
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for i in range (0, len(data) - 1, 1):
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data_t.append(float(data[i]))
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_mean = mean(data_t)
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@ -403,19 +405,14 @@ def basic_stats(data, method, arg): # data=array, mode = ['1d':1d_basic_stats, '
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_mode = None
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try:
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_stdev = stdev(data_t)
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except:
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_stdev = None
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try:
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_variance = variance(data_t)
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except:
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_variance = None
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out = [_mean, _median, _mode, _stdev, _variance]
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return out
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return [_mean, _median, _mode, _stdev, _variance]
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elif method == "column" or method == 1:
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@ -443,9 +440,7 @@ def basic_stats(data, method, arg): # data=array, mode = ['1d':1d_basic_stats, '
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except:
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_variance = None
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out = [_mean, _median, _mode, _stdev, _variance]
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return out
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return [_mean, _median, _mode, _stdev, _variance]
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elif method == "row" or method == 2:
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@ -469,9 +464,8 @@ def basic_stats(data, method, arg): # data=array, mode = ['1d':1d_basic_stats, '
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except:
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_variance = None
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out = [_mean, _median, _mode, _stdev, _variance]
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return [_mean, _median, _mode, _stdev, _variance]
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return out
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else:
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raise error("method error")
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@ -482,17 +476,12 @@ def z_score(point, mean, stdev): #returns z score with inputs of point, mean and
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def stdev_z_split(mean, stdev, delta, low_bound, high_bound): #returns n-th percentile of spread given mean, standard deviation, lower z-score, and upper z-score
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z_split = []
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i = low_bound
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while True:
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z_split.append(float((1 / (stdev * math.sqrt(2 * math.pi))) * math.e ** (-0.5 * (((i - mean) / stdev) ** 2))))
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i = i + delta
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if i > high_bound:
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break
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return z_split
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@ -546,15 +535,12 @@ def histo_analysis(hist_data, delta, low_bound, high_bound):
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i = low_bound
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while True:
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if i > high_bound:
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break
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try:
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pred_change = mean_derivative + i * stdev_derivative
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except:
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pred_change = mean_derivative
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predictions.append(float(hist_data[-1:][0]) + pred_change)
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@ -566,21 +552,16 @@ def histo_analysis(hist_data, delta, low_bound, high_bound):
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def poly_regression(x, y, power):
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if x == "null": #if x is 'null', then x will be filled with integer points between 1 and the size of y
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x = []
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for i in range(len(y)):
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print(i)
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x.append(i+1)
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reg_eq = scipy.polyfit(x, y, deg = power)
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eq_str = ""
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for i in range(0, len(reg_eq), 1):
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if i < len(reg_eq)- 1:
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eq_str = eq_str + str(reg_eq[i]) + "*(z**" + str(len(reg_eq) - i - 1) + ")+"
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else:
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@ -590,11 +571,9 @@ def poly_regression(x, y, power):
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for i in range(0, len(x), 1):
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z = x[i]
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exec("vals.append(" + eq_str + ")")
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_rms = rms(vals, y)
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r2_d2 = r_squared(vals, y)
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return [eq_str, _rms, r2_d2]
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@ -604,23 +583,17 @@ def log_regression(x, y, base):
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x_fit = []
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for i in range(len(x)):
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x_fit.append(np.log(x[i]) / np.log(base)) #change of base for logs
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reg_eq = np.polyfit(x_fit, y, 1) # y = reg_eq[0] * log(x, base) + reg_eq[1]
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eq_str = str(reg_eq[0]) + "* (np.log(z) / np.log(" + str(base) +"))+" + str(reg_eq[1])
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vals = []
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for i in range(len(x)):
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z = x[i]
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exec("vals.append(" + eq_str + ")")
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_rms = rms(vals, y)
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r2_d2 = r_squared(vals, y)
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return [eq_str, _rms, r2_d2]
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@ -630,23 +603,17 @@ def exp_regression(x, y, base):
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y_fit = []
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for i in range(len(y)):
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y_fit.append(np.log(y[i]) / np.log(base)) #change of base for logs
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reg_eq = np.polyfit(x, y_fit, 1, w=np.sqrt(y_fit)) # y = base ^ (reg_eq[0] * x) * base ^ (reg_eq[1])
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eq_str = "(" + str(base) + "**(" + str(reg_eq[0]) + "*z))*(" + str(base) + "**(" + str(reg_eq[1]) + "))"
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vals = []
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for i in range(len(x)):
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z = x[i]
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exec("vals.append(" + eq_str + ")")
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_rms = rms(vals, y)
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r2_d2 = r_squared(vals, y)
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return [eq_str, _rms, r2_d2]
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@ -660,25 +627,17 @@ def r_squared(predictions, targets): # assumes equal size inputs
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def rms(predictions, targets): # assumes equal size inputs
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out = 0
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_sum = 0
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avg = 0
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for i in range(0, len(targets), 1):
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_sum = (targets[i] - predictions[i]) ** 2
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avg = _sum/len(targets)
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out = math.sqrt(avg)
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return float(out)
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return float(math.sqrt(_sum/len(targets)))
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def calc_overfit(equation, rms_train, r2_train, x_test, y_test):
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#overfit = performance(train) - performance(test) where performance is r^2
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#overfir = error(train) - error(test) where error is rms
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#performance overfit = performance(train) - performance(test) where performance is r^2
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#error overfit = error(train) - error(test) where error is rms; biased towards smaller values
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vals = []
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@ -696,107 +655,79 @@ def calc_overfit(equation, rms_train, r2_train, x_test, y_test):
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def strip_data(data, mode):
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if mode == "adam": #x is the row number, y are the data
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pass
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if mode == "eve": #x are the data, y is the column number
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pass
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else:
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raise error("mode error")
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def optimize_regression(x, y, _range, resolution):#_range in poly regression is the range of powers tried, and in log/exp it is the inverse of the stepsize taken from -1000 to 1000
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#usage not: for demonstration purpose only, performance is shit
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if type(resolution) != int:
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raise error("resolution must be int")
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x = x
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y = y
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x_train = []
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y_train = []
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x_train = x
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y_train = y
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x_test = []
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y_test = []
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for i in range (0, math.floor(len(x) * 0.4), 1):
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index = random.randint(0, len(x) - 1)
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x_test.append(x[index])
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y_test.append(y[index])
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x.pop(index)
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y.pop(index)
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x_train = x
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y_train = y
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x_train.pop(index)
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y_train.pop(index)
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#print(x_train, x_test)
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#print(y_train, y_test)
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eqs = []
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rmss = []
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r2s = []
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for i in range (0, _range + 1, 1):
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eqs.append(poly_regression(x_train, y_train, i)[0])
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rmss.append(poly_regression(x_train, y_train, i)[1])
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r2s.append(poly_regression(x_train, y_train, i)[2])
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for i in range (1, 100 * resolution + 1):
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try:
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eqs.append(exp_regression(x_train, y_train, float(i / resolution))[0])
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rmss.append(exp_regression(x_train, y_train, float(i / resolution))[1])
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r2s.append(exp_regression(x_train, y_train, float(i / resolution))[2])
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except:
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pass
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for i in range (1, 100 * resolution + 1):
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try:
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eqs.append(log_regression(x_train, y_train, float(i / resolution))[0])
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rmss.append(log_regression(x_train, y_train, float(i / resolution))[1])
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r2s.append(log_regression(x_train, y_train, float(i / resolution))[2])
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except:
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pass
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for i in range (0, len(eqs), 1): #marks all equations where r2 = 1 as they 95% of the time overfit the data
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if r2s[i] == 1:
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eqs[i] = ""
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rmss[i] = ""
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r2s[i] = ""
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while True: #removes all equations marked for removal
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try:
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eqs.remove('')
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rmss.remove('')
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r2s.remove('')
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except:
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break
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overfit = []
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for i in range (0, len(eqs), 1):
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overfit.append(calc_overfit(eqs[i], rmss[i], r2s[i], x_test, y_test))
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return eqs, rmss, r2s, overfit
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@ -809,7 +740,6 @@ def basic_analysis(filepath): #assumes that rows are the independent variable an
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column = []
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for i in range(0, row, 1):
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column.append(len(data[i]))
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column_max = max(column)
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@ -844,11 +774,9 @@ def generate_data(filename, x, y, low, high):
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file = open(filename, "w")
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for i in range (0, y, 1):
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temp = ""
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for j in range (0, x - 1, 1):
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temp = str(random.uniform(low, high)) + "," + temp
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temp = temp + str(random.uniform(low, high))
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@ -906,18 +834,15 @@ def debug():
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print("--------------------------------")
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print(poly_regression([1, 2, 3, 4, 5], [1, 2, 4, 8, 16], 2))
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print(log_regression([1, 2, 3, 4], [2, 4, 8, 16], 2.717))
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print(exp_regression([1, 2, 3, 4], [2, 4, 8, 16], 2.717))
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x, y, z = optimize_regression([0, 1, 2, 3, 4], [1, 2, 4, 7, 19], 10, 100)
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for i in range(0, len(x), 1):
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print(str(x[i]) + " | " + str(y[i]) + " | " + str(z[i]))
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#statistics def below------------------------------------------------------------------------------------------------------------------------------------------------------
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#statistics def below
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class StatisticsError(ValueError):
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pass
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