diff options
Diffstat (limited to 'examples/example_detect_outliers.py')
| -rw-r--r-- | examples/example_detect_outliers.py | 34 |
1 files changed, 14 insertions, 20 deletions
diff --git a/examples/example_detect_outliers.py b/examples/example_detect_outliers.py index cd997e8..08ae5f5 100644 --- a/examples/example_detect_outliers.py +++ b/examples/example_detect_outliers.py @@ -3,7 +3,7 @@ Outlier detection via leave-one-out =================================== Outliers can sometimes be identified by assessing the influence of each -datapoint. To assess the influence of one point, we fit the dataset while the +datapoint. To assess the influence of one point, we fit the dataset without the point and compare the result with the fit of the full dataset. The code below shows how to do this with lmfit. Note that the presented method is very basic. """ @@ -14,10 +14,8 @@ import numpy as np import lmfit -plt.rcParams['figure.dpi'] = 130 -plt.rcParams['figure.autolayout'] = True ############################################################################### -# Generate test data and model. Apply the model to the data +# Generate test data and model: x = np.linspace(0.3, 10, 100) np.random.seed(1) y = 1.0 / (0.1 * x) + 2.0 + 3 * np.random.randn(x.size) @@ -30,25 +28,27 @@ def func(x, a, b): return 1.0 / (a * x) + b -# Make 5 points outliers +############################################################################### +# Make five points outliers: idx = np.random.randint(0, x.size, 5) y[idx] += 10 * np.random.randn(idx.size) -# Fit the data +############################################################################### +# Fit the data: model = lmfit.Model(func, independent_vars=['x']) fit_result = model.fit(y, x=x, a=0.1, b=2) ############################################################################### # and gives the plot and fitting results below: - fit_result.plot_fit() -plt.plot(x[idx], y[idx], 'o', color='r', label='outliers') +plt.plot(x[idx], y[idx], 'o', label='outliers') plt.show() -print(fit_result.fit_report()) ############################################################################### -# Fit the dataset while omitting one data point +print(fit_result.fit_report()) +############################################################################### +# Fit the dataset while omitting one data point: best_vals = defaultdict(lambda: np.zeros(x.size)) stderrs = defaultdict(lambda: np.zeros(x.size)) chi_sq = np.zeros_like(x) @@ -56,9 +56,7 @@ for i in range(x.size): idx2 = np.arange(0, x.size) idx2 = np.delete(idx2, i) tmp_x = x[idx2] - tmp = model.fit(y[idx2], - x=tmp_x, - a=fit_result.params['a'], + tmp = model.fit(y[idx2], x=tmp_x, a=fit_result.params['a'], b=fit_result.params['b']) chi_sq[i] = tmp.chisqr for p in tmp.params: @@ -67,21 +65,17 @@ for i in range(x.size): stderrs[p][i] = (tpar.stderr / fit_result.params[p].stderr) ############################################################################### -# Plot the influence on the red. chisqr of each point - +# Plot the influence on the red. chisqr of each point: fig, ax = plt.subplots() ax.plot(x, (fit_result.chisqr - chi_sq) / chi_sq) -ax.scatter(x[idx], - fit_result.chisqr / chi_sq[idx] - 1, - color='r', +ax.scatter(x[idx], fit_result.chisqr / chi_sq[idx] - 1, color='r', label='outlier') ax.set_ylabel(r'Relative red. $\chi^2$ change') ax.set_xlabel('x') ax.legend() ############################################################################### -# Plot the influence on the parameter value and error of each point - +# Plot the influence on the parameter value and error of each point: fig, axs = plt.subplots(4, figsize=(4, 7), sharex='col') axs[0].plot(x, best_vals['a']) axs[0].scatter(x[idx], best_vals['a'][idx], color='r', label='outlier') |