From 274b5463c6588043f6e83b8f51e644a28b5fb37f Mon Sep 17 00:00:00 2001 From: Sofia Samaniego Date: Tue, 23 Jun 2026 09:35:47 -0600 Subject: [PATCH] feat: integra cuaderno main.ipynb y genera README en markdown de forma automatica --- Regresion_Logistica/README.md | 6881 ++++++++++++++++++++++++++++++++ Regresion_Logistica/main.ipynb | 711 ++-- 2 files changed, 7278 insertions(+), 314 deletions(-) create mode 100644 Regresion_Logistica/README.md diff --git a/Regresion_Logistica/README.md b/Regresion_Logistica/README.md new file mode 100644 index 0000000..28309cc --- /dev/null +++ b/Regresion_Logistica/README.md @@ -0,0 +1,6881 @@ +# Titulo del proyecto + +# Module 1: Logistic Regression Classifier + +Author: Sofia Samaniego Lopez + +Institution: Universidad Autonoma de Baja California (UABC) + +Advisor: Dr. Gerardo Marx Chavez Campos + +This script establishes the baseline evaluation for binary classification using +the classic Iris dataset. The objective of this specific work is to +analyze linear combination, decision boundaries, and probability estimation based +on morphological features—specifically petal width and length. + +The model utilizes Scikit-Learn's LogisticRegression framework to execute the +optimization process and map inputs to a probability output using the Sigmoid function. +This code serves as a strict benchmark comparison, generating the reference +ground truth metrics and spatial visualizations required to evaluate the performance +and accuracy of subsequent custom implementations. + +## Experimental Setup and Preliminary Analysis + +### Step 1: Import Required Libraries and Environment Setup + +In this initial stage, the necessary scientific computing and data processing libraries are imported to set up our development environment: +* **NumPy**: Utilized for efficient multi-dimensional array operations and core matrix algebra. +* **Matplotlib (pyplot)**: Employed to generate the spatial scatter plots and map the resulting decision boundaries. +* **Scikit-Learn**: Specifically importing the `datasets` module to fetch the target morphological data and `LogisticRegression` to serve as our validation benchmark baseline. + + +```python +!pip3 install scikit-learn +!pip3 install matplotlib +!pip3 install numpy + +import matplotlib.pyplot as plt +import numpy as np +``` + +### Step 2: Load and Explore the Iris Dataset Characteristics + +The classic **Iris Dataset** is loaded into the workspace to establish our baseline classification task. + +#### Dataset Overview and Morphological Features +The complete dataset consists of **150 samples** from three distinct species of Iris flowers (*Iris setosa*, *Iris versicolor*, and *Iris virginica*). For each sample, four continuous geometric features are available: +1. Sepal Length +2. Sepal Width +3. Petal Length +4. Petal Width + +#### Problem Simplification & Single-Feature Evaluation + +The classification task is constrained to analyze the Sigmoid function and linear thresholds on a simpler scale: + +* **Target Binarization:** The multi-class target is converted to binary labels ($y=1$ for *Iris virginica*, $y=0$ for others) to establish a clear threshold. +* **Single-Feature Isolation:** Instead of combining dimensions, features are evaluated **one at a time** (e.g., petal width or sepal length) to inspect their independent separation power before building multi-dimensional models. + + +```python +from sklearn import datasets +iris=datasets.load_iris() +print(iris.DESCR) +``` + + .. _iris_dataset: + + Iris plants dataset + -------------------- + + **Data Set Characteristics:** + + :Number of Instances: 150 (50 in each of three classes) + :Number of Attributes: 4 numeric, predictive attributes and the class + :Attribute Information: + - sepal length in cm + - sepal width in cm + - petal length in cm + - petal width in cm + - class: + - Iris-Setosa + - Iris-Versicolour + - Iris-Virginica + + :Summary Statistics: + + ============== ==== ==== ======= ===== ==================== + Min Max Mean SD Class Correlation + ============== ==== ==== ======= ===== ==================== + sepal length: 4.3 7.9 5.84 0.83 0.7826 + sepal width: 2.0 4.4 3.05 0.43 -0.4194 + petal length: 1.0 6.9 3.76 1.76 0.9490 (high!) + petal width: 0.1 2.5 1.20 0.76 0.9565 (high!) + ============== ==== ==== ======= ===== ==================== + + :Missing Attribute Values: None + :Class Distribution: 33.3% for each of 3 classes. + :Creator: R.A. Fisher + :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov) + :Date: July, 1988 + + The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken + from Fisher's paper. Note that it's the same as in R, but not as in the UCI + Machine Learning Repository, which has two wrong data points. + + This is perhaps the best known database to be found in the + pattern recognition literature. Fisher's paper is a classic in the field and + is referenced frequently to this day. (See Duda & Hart, for example.) The + data set contains 3 classes of 50 instances each, where each class refers to a + type of iris plant. One class is linearly separable from the other 2; the + latter are NOT linearly separable from each other. + + .. dropdown:: References + + - Fisher, R.A. "The use of multiple measurements in taxonomic problems" + Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to + Mathematical Statistics" (John Wiley, NY, 1950). + - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis. + (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218. + - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System + Structure and Classification Rule for Recognition in Partially Exposed + Environments". IEEE Transactions on Pattern Analysis and Machine + Intelligence, Vol. PAMI-2, No. 1, 67-71. + - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions + on Information Theory, May 1972, 431-433. + - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II + conceptual clustering system finds 3 classes in the data. + - Many, many more ... + + + +### Step 3: Exploratory Data Analysis & Target Inspection + +Prior to model optimization, a visual and structural inspection evaluates the data distribution: + +* **Unclassified Spatial Mapping:** Plotting Sepal Length (`sl`) vs. Sepal Width (`sw`) using plain markers (`'.k'`) reveals the raw data structure. This helps verify if the morphological features naturally form distinct clusters before applying any algorithmic boundaries. +* **Target Label Mapping (`iris.target`):** Inspecting the ground-truth array to map the multi-class numerical taxonomy: + * `0`: *Iris setosa* + * `1`: *Iris versicolor* + * `2`: *Iris virginica* + +This inspection links the visual spatial groups with their mathematical labels, establishing the baseline before data binarization. + + +```python +sl = iris.data[:, 0:1] +sw = iris.data[:, 1:2] +plt.plot(sl,sw,'.k') +plt.show() +``` + + + +![png](README_files/README_10_0.png) + + + + +```python +iris.target +``` + + + + + array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, + 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, + 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) + + + +### Step 4: Theoretical Sigmoid Function & Decision Boundary + +Generating a synthetic domain from -10 to 10 to plot the standalone mathematical Sigmoid function: + +$$\sigma(t) = \frac{1}{1 + e^{-t}}$$ + +This visualizes how the curve maps inputs to a probability between 0 and 1, establishing the inflection point $\sigma(0) = 0.5$ as the theoretical threshold for the decision boundary. + + +```python +t= np.linspace(-10,10,100) +sig = 1/(1+np.exp(-t)) +plt.plot(t,sig, '.b', label=r"$\sigma$") +plt.legend(loc='upper left', fontsize=20) +plt.show() +``` + + + +![png](README_files/README_13_0.png) + + + +## Model Training and Benchmark Evaluation + +### Model 1: Iris-Setosa Classifier based on petal width + +#### Feature Selection & Setosa Target Binarization + +The dataset is filtered and restructured to evaluate a new binary classification task: +* **Feature Vector ($X$):** Slicing index `[:, 3:]` isolates **Petal Width** as the continuous predictor variable. +* **Target Binarization ($y$):** The criteria `(iris.target == 0).astype(int)` shifts the positive class ($y=1$) exclusively to **Iris setosa**, mapping all other species to $0$. This creates the target array seen in the output. + + +```python +x = iris.data[:, 3:] +y = (iris.target == 0).astype(int) +y +``` + + + + + array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) + + + +#### Benchmark Model Initialization and Fitting + +This cell instantiates and trains the baseline classification model using Scikit-Learn: + +* **`LogisticRegression(solver='lbfgs', random_state=42)`**: Instantiates the model using the Limited-memory BFGS (`lbfgs`) optimization solver and locks the `random_state` to 42 to ensure reproducible weight initialization. +* **`mylr.fit(x, y)`**: Trains the classifier on the isolated feature vector `x` and the binarized target labels `y`. This optimization process computes the optimal weight ($w$) and bias ($b$) parameters that minimize the loss function. + + +```python +from sklearn.linear_model import LogisticRegression +mylr = LogisticRegression(solver='lbfgs', random_state=42) +mylr.fit(x,y) +``` + + + + +
LogisticRegression(random_state=42)
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+ + + + +```python +Xnew = np.linspace(-1,3,100).reshape(-1,1) +yPred = mylr.predict_proba(Xnew) +#plt.plot(Xnew, yPred[:,0], label= 'No Iris') +plt.plot(Xnew, yPred[:,1], label= 'Yes Iris') +plt.legend() +plt.plot(x,y,'*g') +plt.show() +``` + + + +![png](README_files/README_20_0.png) + + + +This plot visualizes the trained model's Sigmoid prediction curve over the experimental dataset samples: + +* **Sample Distribution (Green Stars):** Represents the real dataset. Small petal widths (0.1 - 0.6 cm) belong to *Iris setosa* ($y=1$), while larger widths (1.0 - 2.5 cm) belong to the other species ($y=0$). +* **Sigmoid Mapping (Blue Curve):** Displays an inverted logistic curve. It demonstrates the mathematical relationship: as petal width increases, the probability of the flower being *Iris setosa* drops sharply from 1.0 to 0.0. +* **Decision Boundary Threshold:** The curve crosses the 0.5 probability threshold at approximately 0.75 cm. This inflection point defines the exact baseline boundary separating both classifications. + +### Model 2: Iris-Setosa Classifier based on petal length + +#### Feature Shift – Petal Length Isolation + +The model configuration is updated to evaluate a different morphological predictor: +* **Feature Vector ($X$):** Slicing index `[:, 2:3]` isolates **Petal Length** as the independent variable. +* **Target Continuity ($y$):** The classification objective remains focused on **Iris setosa** (`iris.target == 0`) to compare the separation power of petal length against the previous petal width baseline. + + +```python +x = iris.data[:, 2:3] +y = (iris.target == 0).astype(int) + +``` + + +```python +from sklearn.linear_model import LogisticRegression +mylr = LogisticRegression(solver='lbfgs', random_state=42) +mylr.fit(x,y) +``` + + + + +
LogisticRegression(random_state=42)
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+ + + + +```python +Xnew = np.linspace(0,8,100).reshape(-1,1) +yPred = mylr.predict_proba(Xnew) +#plt.plot(Xnew, yPred[:,0], label= 'No Iris') +plt.plot(Xnew, yPred[:,1], label= 'Yes Iris') +plt.legend() +plt.plot(x,y,'*g') +plt.axis([1.5, 5, -0.5, 1.5]) +plt.show() +``` + + + +![png](README_files/README_26_0.png) + + + +This plot illustrates the performance of the second univariable model using **Petal Length**: + +* **Sample Distribution:** Samples with short petal lengths (1.0 - 2.0 cm) are correctly clustered as *Iris setosa* ($y=1$), while samples with larger lengths ($>3.0$ cm) map to $y=0$. +* **Sigmoid Mapping:** The descending blue curve demonstrates that as petal length increases, the probability of the sample being *Iris setosa* drops sharply from 1.0 to 0.0. +* **Decision Boundary:** The curve crosses the 0.5 probability threshold at approximately 2.5 cm, marking the exact inflection point that separates the target class from the rest of the dataset. + +### Model 3: Iris-Setosa Classifier based on Sepal length + +#### Feature Shift – Sepal Length Isolation + +The model evaluates a third morphological predictor independently: +* **Feature Vector ($X$):** Slicing index `[:, 0:1]` isolates **Sepal Length** as the continuous independent variable. +* **Target Continuity ($y$):** The objective remains focused on **Iris setosa** ($y=1$) to compare the separation power of sepal dimensions against the previous petal metrics. + + +```python +x = iris.data[:, 0:1] +y = (iris.target == 0).astype(int) +from sklearn.linear_model import LogisticRegression +mylr = LogisticRegression(solver='lbfgs', random_state=42) +mylr.fit(x,y) +``` + + + + +
LogisticRegression(random_state=42)
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+ + + + +```python +Xnew = np.linspace(0,8,100).reshape(-1,1) +yPred = mylr.predict_proba(Xnew) +#plt.plot(Xnew, yPred[:,0], label= 'No Iris') +plt.plot(Xnew, yPred[:,1], label= 'Yes Iris') +plt.legend() +plt.plot(x,y,'*g') +plt.axis([3.5, 7, -0.1, 1.1]) +plt.show() +``` + + + +![png](README_files/README_31_0.png) + + + +This plot displays the performance of the third univariable model using **Sepal Length**: + +* **Sample Distribution:** Samples representing *Iris setosa* ($y=1$) are concentrated at shorter lengths, but show a much higher spatial overlap with non-setosa samples ($y=0$) compared to the previous petal features. +* **Sigmoid Mapping:** The descending curve shows the probability dropping as sepal length increases. Due to this significant data overlap, the slope is less steep, indicating a more gradual and less aggressive probabilistic transition. +* **Decision Boundary:** The inflection point at $\sigma = 0.5$ establishes the final threshold. This boundary carries more classification uncertainty because sepal dimensions are naturally less distinct between these species. + +### Model 4: Multiple features classifier + +#### Multi-Class Spatial Mapping (Sepal Features) + +This cell upgrades the initial exploratory plot by adding the ground-truth class labels to the 2D sepal feature space: + +* **Feature Interaction:** Maps Sepal Length (`sl`) against Sepal Width (`sw`) simultaneously to analyze their combined distribution. +* **Class Color-Coding:** Differentiates the three original species using distinct markers: Green for *Setosa*, Red for *Versicolor*, and Blue for *Virginica*. +* **Visual Separability Analysis:** Allows immediate observation of the data structure, showing that while *Setosa* forms a perfectly isolated cluster, *Versicolor* and *Virginica* exhibit significant spatial overlap, justifying the need for optimization models. + + +```python +import matplotlib.pyplot as plt +sl = iris.data[:,0:1] +sw = iris.data[:,1:2] +tg = iris.target +plt.plot(sl[tg==0,0], sw[tg==0,0],'.g' ,label='Set') +plt.plot(sl[tg==1,0], sw[tg==1,0],'.r', label='Ver') +plt.plot(sl[tg==2,0], sw[tg==2,0],'.b', label='Vir') +plt.legend() +plt.show() +``` + + + +![png](README_files/README_35_0.png) + + + +#### Bivariate Model Training for Iris Virginica + +This cell configures and trains a multi-feature logistic regression model utilizing tuned optimization parameters: + +* **Bivariate Data Selection:** * **Features (`X`):** Slices index `[:, 0:2]` to combine **Sepal Length** and **Sepal Width** into a two-dimensional feature space. + * **Target (`y`):** Shifts the positive class focus exclusively to **Iris virginica** (`iris.target == 2`). +* **Hyperparameter Tuning (`mylrvir`):** + * **`solver='newton-cg'`**: Uses the Newton-Conjugate Gradient method to compute accurate optimization paths. + * **`C=100` & `tol=1e-5`**: Applies high cost (low regularization) to allow a tighter fit to the data, paired with a strict tolerance for precise convergence. +* **`mylrvir.fit(X, y)`**: Trains the system to find the optimal weight vector $w = [w_1, w_2]$ and bias ($b$), establishing the multi-variable benchmark line. + + +```python +X = iris.data[:,0:2] +y = (iris.target==2).astype(int) +mylrvir = LogisticRegression( + random_state=22, + tol=1e-5, + C=100, + max_iter=100, + solver='newton-cg' +) +mylrvir.fit(X,y) +``` + + + + +
LogisticRegression(C=100, random_state=22, solver='newton-cg', tol=1e-05)
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+ + + +#### Coordinate Grid Generation & Probability Mapping + +This block builds the mathematical testing grid used to map out the complete probability landscape: + +* **`np.meshgrid`**: Generates a dense $100 \times 100$ coordinate grid across the sepal feature space (Length: 3–8 cm, Width: 0–6 cm). +* **`Xnew` (`np.c_`)**: Flattens and couples the grid matrices into a matrix of 10,000 discrete 2D spatial coordinates. +* **`predict_proba(Xnew)`**: Evaluates the trained model across the entire grid, computing the continuous probabilities needed to render the decision contours and 3D surfaces. + + +```python +x0, x1 = np.meshgrid( + np.linspace(3,8,100).reshape(-1,1), + np.linspace(0,6,100).reshape(-1,1) +) +Xnew = np.c_[x0.ravel(), x1.ravel()] +yPred = mylrvir.predict_proba(Xnew) +``` + + +```python +plt.figure(figsize=(10,4)) +plt.plot(X[y==0,0], X[y==0,1],'bs',label='No Virg') +plt.plot(X[y==1,0], X[y==1,1],'g^',label='Virginica') +zz=yPred[:,1].reshape(x0.shape) +contour=plt.contour(x0,x1,zz) +plt.clabel(contour, inline=1,fontsize=15) +plt.xlabel("Sepal Length") +plt.ylabel("Sepal Width") +plt.legend() +plt.show() +``` + + + +![png](README_files/README_40_0.png) + + + +This plot visualizes the continuous probability space generated by the trained bivariate model: + +* **Sample Distribution:** Blue squares represent non-virginica samples ($y=0$), and green triangles represent *Iris virginica* ($y=1$) mapped across Sepal Length and Sepal Width. +* **Probability Contours (`plt.contour`):** The labeled contour lines map specific probability thresholds. They show how the model's prediction confidence transitions across the 2D space. +* **Decision Boundary:** The contour line labeled **0.5** marks the exact geometric threshold. Any sample falling past this line is classified as *Iris virginica*, capturing the spatial trade-off between both sepal measurements. + + +```python +fig, ax =plt.subplots(subplot_kw={"projection": "3d"}) +surf = ax.plot_surface(x0,x1,zz, cmap='jet') +ax.scatter(iris.data[:,0:1], iris.data[:,1:2], y, 'or') +``` + + + + + + + + + + +![png](README_files/README_42_1.png) + + + +This cell projects the bivariate logistic regression model into a 3D coordinate space to visualize the complete probability landscape: + +* **Axis Dimensions:** The horizontal axes represent **Sepal Length** ($x_1$) and **Sepal Width** ($x_2$), while the vertical axis ($Z$) tracks the continuous model probability $\sigma(z) \in [0, 1]$. +* **Probability Surface (`plot_surface`):** The Sigmoid function is rendered as a 3D sheet using the `jet` colormap. It displays the non-linear S-curve transition dynamically stretched across the two-dimensional feature plane. +* **True Labels Spatial Scatter (`ax.scatter`):** The red markers plot the actual samples at their exact spatial coordinates and true binary height ($z = 1$ for *Iris virginica*, $z = 0$ for others). This highlights how the optimized surface splits the space to fit the data points. + +### Modelo 5: Multiple features and muticlass classifier + +#### Multi-Feature & Multi-Class Model Training + +This cell configures and trains the final baseline model to handle all three species simultaneously within a two-dimensional feature space: + +* **Bivariate Inputs (`X`):** Slices index `[:, 0:2]` to utilize both **Sepal Length** and **Sepal Width** as the predictor variables. +* **Multi-Class Target (`y`):** Retains the original multi-class target labels (`0`, `1`, `2`) without applying binarization, expanding the task from a single boundary to a three-class decision space. +* **`LogisticRegression(C=100, solver='lbfgs')`:** Trains a multinomial classifier. The algorithm optimizes a distinct set of weights and biases for each target category, preparing the model to partition the 2D plane into three distinct classification zones. + + +```python +X = iris.data[:,0:2] +y = iris.target +lrmc = LogisticRegression( + solver='lbfgs', + C=100, + random_state=22 +) +lrmc.fit(X,y) +``` + + + + +
LogisticRegression(C=100, random_state=22)
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+ + + +#### Multi-Class Grid Generation and Probability Evaluation + +This cell sets up the coordinate testing matrix to evaluate the multi-class prediction behavior across the entire sepal feature space: + +* **`np.meshgrid`**: Constructs a dense $100 \times 100$ coordinate grid bounding the Sepal Length (3–8 cm) and Sepal Width (0–6 cm) ranges. +* **`Xnew` (`np.c_`)**: Flattens and pairs the grid elements into a matrix of 10,000 distinct 2D spatial coordinates. +* **`lrmc.predict_proba(Xnew)`**: Computes a three-column probability matrix for each point. This mapping determines the exact multi-class boundaries by evaluating the localized likelihood for *Setosa*, *Versicolor*, and *Virginica* simultaneously. + + +```python +x0, x1 = np.meshgrid( + np.linspace(3,8,100).reshape(-1,1), + np.linspace(0,6,100).reshape(-1,1) +) +Xnew = np.c_[x0.ravel(), x1.ravel()] +yPred = lrmc.predict_proba(Xnew) +``` + + +```python +plt.figure(figsize=(10,4)) +plt.plot(X[y==0,0], X[y==0,1],'.b',label='Setosa') +plt.plot(X[y==1,0], X[y==1,1],'+g',label='Versi') +plt.plot(X[y==2,0], X[y==2,1],'*m',label='Virgi') +zz=yPred[:,1].reshape(x0.shape) +contour=plt.contour(x0,x1,zz) +plt.clabel(contour, inline=1,fontsize=15) +plt.xlabel("Sepal Length") +plt.ylabel("Sepal Width") +plt.legend() +plt.show() +``` + + + +![png](README_files/README_49_0.png) + + + +This plot maps the continuous probability distribution of the middle class within the multi-class decision space: + +* **Three-Class Distribution:** Displays all species simultaneously using distinct markers: blue dots for *Setosa* ($y=0$), green pluses for *Versicolor* ($y=1$), and magenta stars for *Virginica* ($y=2$). +* **Target Class Extraction (`yPred[:, 1]`):** Slicing the second column of the probability matrix isolates and tracks the specific localized likelihood of a sample being **Iris versicolor**. +* **Localized Probability Ridge:** Unlike the linear boundaries seen in binary classification, the multinomial model creates a bounded peak or "ridge" to isolate the middle class. The highest contour line (**0.90**) tightly encapsulates the core *Versicolor* cluster, dropping off systematically as the features move toward *Setosa* (left) or *Virginica* (right) territories. + + +```python +yPred = lrmc.predict(Xnew) +plt.figure(figsize=(10,6)) +plt.plot(X[y==0,0], X[y==0,1],'bs',label='Setosa') +plt.plot(X[y==1,0], X[y==1,1],'g^',label='Versi') +plt.plot(X[y==2,0], X[y==2,1],'*m',label='Virgi') +zz=yPred.reshape(x0.shape) +contour=plt.contourf(x0,x1,zz, cmap='jet', alpha=0.3) +plt.clabel(contour, inline=1,fontsize=15) +plt.xlabel("Sepal Length") +plt.ylabel("Sepal Width") +plt.legend() +plt.show() +``` + + + +![png](README_files/README_51_0.png) + + + +This plot visualizes the ultimate classification boundaries by partitioning the entire 2D feature space into hard decision zones: + +* **Hard Class Assignment (`lrmc.predict`):** Converts continuous probabilities into discrete class verdicts (`0`, `1`, or `2`) by applying an *argmax* function (selecting the class with the highest probability for each point). +* **Filled Decision Regions (`plt.contourf`):** Shades the coordinate plane into three distinct, solid zones using the `jet` colormap: + * **Blue Region:** Absolute classification space for *Iris setosa*. + * **Green Region:** Absolute classification space for *Iris versicolor*. + * **Red/Orange Region:** Absolute classification space for *Iris virginica*. +* **Boundary Analysis:** The sharp geometric intersections between the colored blocks define the definitive decision thresholds. This layout explicitly reveals how the linear multi-class model manages the regional trade-offs and handles the spatial overlap between the *Versicolor* and *Virginica* samples. + + +```python +fig, ax =plt.subplots(subplot_kw={"projection": "3d"}) +surf = ax.plot_surface(x0,x1,zz, cmap='jet') +ax.scatter(iris.data[:,0:1], iris.data[:,1:2], y, 'or') +``` + + + + + + + + + + +![png](README_files/README_53_1.png) + + + +This cell integrates the actual dataset samples into the 3D hard decision space to visually evaluate the multi-class model's accuracy: + +* **Discrete Vertical Alignment ($Z$):** Both the staircase surface and the scatter markers use the integer multi-class taxonomy ($0$ for *Setosa*, $1$ for *Versicolor*, and $2$ for *Virginica*) instead of continuous probabilities. +* **Stepped Surface (`plot_surface`):** Represents the geometric boundaries computed by the model. Each level dictates the categorical verdict zone based on the combination of sepal features. +* **True Labels Scatter (`ax.scatter`):** Plots the real flower samples at their actual feature coordinates and true species height. This allows immediate visual verification of performance: samples resting on their matching colored step are correctly classified, while those caught on the wrong tier highlight the exact instances of classification error caused by spatial overlap. diff --git a/Regresion_Logistica/main.ipynb b/Regresion_Logistica/main.ipynb index 5104f7e..1046f60 100644 --- a/Regresion_Logistica/main.ipynb +++ b/Regresion_Logistica/main.ipynb @@ -1,265 +1,113 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "59183b2f", + "metadata": {}, + "source": [ + "# Titulo del proyecto" + ] + }, + { + "cell_type": "markdown", + "id": "64fd25ad", + "metadata": {}, + "source": [ + "# Module 1: Logistic Regression Classifier" + ] + }, + { + "cell_type": "markdown", + "id": "775ab28d", + "metadata": {}, + "source": [ + "Author: Sofia Samaniego Lopez\n", + "\n", + "Institution: Universidad Autonoma de Baja California (UABC)\n", + "\n", + "Advisor: Dr. Gerardo Marx Chavez Campos" + ] + }, + { + "cell_type": "markdown", + "id": "ee13037f", + "metadata": {}, + "source": [ + "This script establishes the baseline evaluation for binary classification using \n", + "the classic Iris dataset. The objective of this specific work is to \n", + "analyze linear combination, decision boundaries, and probability estimation based \n", + "on morphological features—specifically petal width and length. \n", + "\n", + "The model utilizes Scikit-Learn's LogisticRegression framework to execute the \n", + "optimization process and map inputs to a probability output using the Sigmoid function. \n", + "This code serves as a strict benchmark comparison, generating the reference \n", + "ground truth metrics and spatial visualizations required to evaluate the performance \n", + "and accuracy of subsequent custom implementations." + ] + }, + { + "cell_type": "markdown", + "id": "2cd1439d", + "metadata": {}, + "source": [ + "## Experimental Setup and Preliminary Analysis" + ] + }, + { + "cell_type": "markdown", + "id": "0afe660c", + "metadata": {}, + "source": [ + "### Step 1: Import Required Libraries and Environment Setup\n", + "\n", + "In this initial stage, the necessary scientific computing and data processing libraries are imported to set up our development environment:\n", + "* **NumPy**: Utilized for efficient multi-dimensional array operations and core matrix algebra.\n", + "* **Matplotlib (pyplot)**: Employed to generate the spatial scatter plots and map the resulting decision boundaries.\n", + "* **Scikit-Learn**: Specifically importing the `datasets` module to fetch the target morphological data and `LogisticRegression` to serve as our validation benchmark baseline." + ] + }, { "cell_type": "code", - 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"Requirement already satisfied: numpy in c:\\Users\\sofia\\Programa Delfín\\.venv\\Lib\\site-packages (2.4.6)\n" - ] - } - ], + "outputs": [], "source": [ "!pip3 install scikit-learn\n", "!pip3 install matplotlib\n", - "!pip3 install numpy\n" + "!pip3 install numpy\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "id": "5d2dbd5a", + "metadata": {}, + "source": [ + "### Step 2: Load and Explore the Iris Dataset Characteristics\n", + "\n", + "The classic **Iris Dataset** is loaded into the workspace to establish our baseline classification task. \n", + "\n", + "#### Dataset Overview and Morphological Features\n", + "The complete dataset consists of **150 samples** from three distinct species of Iris flowers (*Iris setosa*, *Iris versicolor*, and *Iris virginica*). For each sample, four continuous geometric features are available:\n", + "1. Sepal Length\n", + "2. Sepal Width\n", + "3. Petal Length\n", + "4. Petal Width\n", + "\n", + "#### Problem Simplification & Single-Feature Evaluation\n", + "\n", + "The classification task is constrained to analyze the Sigmoid function and linear thresholds on a simpler scale:\n", + "\n", + "* **Target Binarization:** The multi-class target is converted to binary labels ($y=1$ for *Iris virginica*, $y=0$ for others) to establish a clear threshold.\n", + "* **Single-Feature Isolation:** Instead of combining dimensions, features are evaluated **one at a time** (e.g., petal width or sepal length) to inspect their independent separation power before building multi-dimensional models." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 11, "id": "cd073961", "metadata": {}, "outputs": [ @@ -341,19 +189,26 @@ ] }, { - "cell_type": "code", - "execution_count": 20, - "id": "0ba60b35", + "cell_type": "markdown", + "id": "2937ca04", "metadata": {}, - "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np" + "### Step 3: Exploratory Data Analysis & Target Inspection\n", + "\n", + "Prior to model optimization, a visual and structural inspection evaluates the data distribution:\n", + "\n", + "* **Unclassified Spatial Mapping:** Plotting Sepal Length (`sl`) vs. Sepal Width (`sw`) using plain markers (`'.k'`) reveals the raw data structure. This helps verify if the morphological features naturally form distinct clusters before applying any algorithmic boundaries.\n", + "* **Target Label Mapping (`iris.target`):** Inspecting the ground-truth array to map the multi-class numerical taxonomy:\n", + " * `0`: *Iris setosa*\n", + " * `1`: *Iris versicolor*\n", + " * `2`: *Iris virginica*\n", + "\n", + "This inspection links the visual spatial groups with their mathematical labels, establishing the baseline before data binarization." ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 12, "id": "958f8009", "metadata": {}, "outputs": [ @@ -377,7 +232,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 13, "id": "211fa51f", "metadata": {}, "outputs": [ @@ -393,7 +248,7 @@ " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])" ] }, - "execution_count": 25, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -407,12 +262,18 @@ "id": "b6bc04d7", "metadata": {}, "source": [ - "# Decision Boundaries" + "### Step 4: Theoretical Sigmoid Function & Decision Boundary\n", + "\n", + "Generating a synthetic domain from -10 to 10 to plot the standalone mathematical Sigmoid function:\n", + "\n", + "$$\\sigma(t) = \\frac{1}{1 + e^{-t}}$$\n", + "\n", + "This visualizes how the curve maps inputs to a probability between 0 and 1, establishing the inflection point $\\sigma(0) = 0.5$ as the theoretical threshold for the decision boundary." ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 14, "id": "9e5198a9", "metadata": {}, "outputs": [ @@ -435,17 +296,37 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "efd88620", + "metadata": {}, + "source": [ + "## Model Training and Benchmark Evaluation" + ] + }, { "cell_type": "markdown", "id": "7f36a094", "metadata": {}, "source": [ - "# Iris-Setosa Classifier based on petal width" + "### Model 1: Iris-Setosa Classifier based on petal width" + ] + }, + { + "cell_type": "markdown", + "id": "8df9d1ac", + "metadata": {}, + "source": [ + "#### Feature Selection & Setosa Target Binarization\n", + "\n", + "The dataset is filtered and restructured to evaluate a new binary classification task:\n", + "* **Feature Vector ($X$):** Slicing index `[:, 3:]` isolates **Petal Width** as the continuous predictor variable.\n", + "* **Target Binarization ($y$):** The criteria `(iris.target == 0).astype(int)` shifts the positive class ($y=1$) exclusively to **Iris setosa**, mapping all other species to $0$. This creates the target array seen in the output." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "004d1192", "metadata": {}, "outputs": [ @@ -461,19 +342,33 @@ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])" ] }, - "execution_count": 38, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = iris.data[:, 3:]\n", - "y = (iris.target == 0).astype(int)" + "y = (iris.target == 0).astype(int)\n", + "y" + ] + }, + { + "cell_type": "markdown", + "id": "40426620", + "metadata": {}, + "source": [ + "#### Benchmark Model Initialization and Fitting\n", + "\n", + "This cell instantiates and trains the baseline classification model using Scikit-Learn:\n", + "\n", + "* **`LogisticRegression(solver='lbfgs', random_state=42)`**: Instantiates the model using the Limited-memory BFGS (`lbfgs`) optimization solver and locks the `random_state` to 42 to ensure reproducible weight initialization.\n", + "* **`mylr.fit(x, y)`**: Trains the classifier on the isolated feature vector `x` and the binarized target labels `y`. This optimization process computes the optimal weight ($w$) and bias ($b$) parameters that minimize the loss function." ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 16, "id": "1069ed1e", "metadata": {}, "outputs": [ @@ -1735,7 +1630,7 @@ "LogisticRegression(random_state=42)" ] }, - "execution_count": 42, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1748,7 +1643,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 17, "id": "dcc26a47", "metadata": {}, "outputs": [ @@ -1773,46 +1668,52 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "454fd592", + "metadata": {}, + "source": [ + "This plot visualizes the trained model's Sigmoid prediction curve over the experimental dataset samples:\n", + "\n", + "* **Sample Distribution (Green Stars):** Represents the real dataset. Small petal widths (0.1 - 0.6 cm) belong to *Iris setosa* ($y=1$), while larger widths (1.0 - 2.5 cm) belong to the other species ($y=0$).\n", + "* **Sigmoid Mapping (Blue Curve):** Displays an inverted logistic curve. It demonstrates the mathematical relationship: as petal width increases, the probability of the flower being *Iris setosa* drops sharply from 1.0 to 0.0.\n", + "* **Decision Boundary Threshold:** The curve crosses the 0.5 probability threshold at approximately 0.75 cm. This inflection point defines the exact baseline boundary separating both classifications." + ] + }, { "cell_type": "markdown", "id": "2732db6f", "metadata": {}, "source": [ - "# Iris-Setosa Classifier based on petal length" + "### Model 2: Iris-Setosa Classifier based on petal length" + ] + }, + { + "cell_type": "markdown", + "id": "fce77a41", + "metadata": {}, + "source": [ + "#### Feature Shift – Petal Length Isolation\n", + "\n", + "The model configuration is updated to evaluate a different morphological predictor:\n", + "* **Feature Vector ($X$):** Slicing index `[:, 2:3]` isolates **Petal Length** as the independent variable.\n", + "* **Target Continuity ($y$):** The classification objective remains focused on **Iris setosa** (`iris.target == 0`) to compare the separation power of petal length against the previous petal width baseline." ] }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 18, "id": "4e428962", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", - " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", - " 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "x = iris.data[:, 2:3]\n", - "y = (iris.target == 0).astype(int)\n", - "y" + "y = (iris.target == 0).astype(int)\n" ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 19, "id": "167b1fa6", "metadata": {}, "outputs": [ @@ -3074,7 +2975,7 @@ "LogisticRegression(random_state=42)" ] }, - "execution_count": 55, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -3087,7 +2988,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 20, "id": "1be05124", "metadata": {}, "outputs": [ @@ -3113,17 +3014,41 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "648dbad1", + "metadata": {}, + "source": [ + "This plot illustrates the performance of the second univariable model using **Petal Length**:\n", + "\n", + "* **Sample Distribution:** Samples with short petal lengths (1.0 - 2.0 cm) are correctly clustered as *Iris setosa* ($y=1$), while samples with larger lengths ($>3.0$ cm) map to $y=0$.\n", + "* **Sigmoid Mapping:** The descending blue curve demonstrates that as petal length increases, the probability of the sample being *Iris setosa* drops sharply from 1.0 to 0.0.\n", + "* **Decision Boundary:** The curve crosses the 0.5 probability threshold at approximately 2.5 cm, marking the exact inflection point that separates the target class from the rest of the dataset." + ] + }, { "cell_type": "markdown", "id": "87ce64b0", "metadata": {}, "source": [ - "# Iris-Setosa Classifier based on Sepal length" + "### Model 3: Iris-Setosa Classifier based on Sepal length" + ] + }, + { + "cell_type": "markdown", + "id": "e58f5ec6", + "metadata": {}, + "source": [ + "#### Feature Shift – Sepal Length Isolation\n", + "\n", + "The model evaluates a third morphological predictor independently:\n", + "* **Feature Vector ($X$):** Slicing index `[:, 0:1]` isolates **Sepal Length** as the continuous independent variable.\n", + "* **Target Continuity ($y$):** The objective remains focused on **Iris setosa** ($y=1$) to compare the separation power of sepal dimensions against the previous petal metrics." ] }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 21, "id": "78ea8db6", "metadata": {}, "outputs": [ @@ -4385,7 +4310,7 @@ "LogisticRegression(random_state=42)" ] }, - "execution_count": 63, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -4400,7 +4325,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 22, "id": "ff8a3c01", "metadata": {}, "outputs": [ @@ -4426,17 +4351,43 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "7e939116", + "metadata": {}, + "source": [ + "This plot displays the performance of the third univariable model using **Sepal Length**:\n", + "\n", + "* **Sample Distribution:** Samples representing *Iris setosa* ($y=1$) are concentrated at shorter lengths, but show a much higher spatial overlap with non-setosa samples ($y=0$) compared to the previous petal features.\n", + "* **Sigmoid Mapping:** The descending curve shows the probability dropping as sepal length increases. Due to this significant data overlap, the slope is less steep, indicating a more gradual and less aggressive probabilistic transition.\n", + "* **Decision Boundary:** The inflection point at $\\sigma = 0.5$ establishes the final threshold. This boundary carries more classification uncertainty because sepal dimensions are naturally less distinct between these species." + ] + }, { "cell_type": "markdown", "id": "344cf161", "metadata": {}, "source": [ - "# Multiple features classifier" + "### Model 4: Multiple features classifier" + ] + }, + { + "cell_type": "markdown", + "id": "7950f0a3", + "metadata": {}, + "source": [ + "#### Multi-Class Spatial Mapping (Sepal Features)\n", + "\n", + "This cell upgrades the initial exploratory plot by adding the ground-truth class labels to the 2D sepal feature space:\n", + "\n", + "* **Feature Interaction:** Maps Sepal Length (`sl`) against Sepal Width (`sw`) simultaneously to analyze their combined distribution.\n", + "* **Class Color-Coding:** Differentiates the three original species using distinct markers: Green for *Setosa*, Red for *Versicolor*, and Blue for *Virginica*.\n", + "* **Visual Separability Analysis:** Allows immediate observation of the data structure, showing that while *Setosa* forms a perfectly isolated cluster, *Versicolor* and *Virginica* exhibit significant spatial overlap, justifying the need for optimization models." ] }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 23, "id": "c2bae4e6", "metadata": {}, "outputs": [ @@ -4463,9 +4414,26 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "5f693453", + "metadata": {}, + "source": [ + "#### Bivariate Model Training for Iris Virginica\n", + "\n", + "This cell configures and trains a multi-feature logistic regression model utilizing tuned optimization parameters:\n", + "\n", + "* **Bivariate Data Selection:** * **Features (`X`):** Slices index `[:, 0:2]` to combine **Sepal Length** and **Sepal Width** into a two-dimensional feature space.\n", + " * **Target (`y`):** Shifts the positive class focus exclusively to **Iris virginica** (`iris.target == 2`).\n", + "* **Hyperparameter Tuning (`mylrvir`):**\n", + " * **`solver='newton-cg'`**: Uses the Newton-Conjugate Gradient method to compute accurate optimization paths.\n", + " * **`C=100` & `tol=1e-5`**: Applies high cost (low regularization) to allow a tighter fit to the data, paired with a strict tolerance for precise convergence.\n", + "* **`mylrvir.fit(X, y)`**: Trains the system to find the optimal weight vector $w = [w_1, w_2]$ and bias ($b$), establishing the multi-variable benchmark line." + ] + }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 24, "id": "ce1caddc", "metadata": {}, "outputs": [ @@ -5727,7 +5695,7 @@ "LogisticRegression(C=100, random_state=22, solver='newton-cg', tol=1e-05)" ] }, - "execution_count": 68, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -5745,9 +5713,23 @@ "mylrvir.fit(X,y)" ] }, + { + "cell_type": "markdown", + "id": "9ba67f6d", + "metadata": {}, + "source": [ + "#### Coordinate Grid Generation & Probability Mapping\n", + "\n", + "This block builds the mathematical testing grid used to map out the complete probability landscape:\n", + "\n", + "* **`np.meshgrid`**: Generates a dense $100 \\times 100$ coordinate grid across the sepal feature space (Length: 3–8 cm, Width: 0–6 cm).\n", + "* **`Xnew` (`np.c_`)**: Flattens and couples the grid matrices into a matrix of 10,000 discrete 2D spatial coordinates.\n", + "* **`predict_proba(Xnew)`**: Evaluates the trained model across the entire grid, computing the continuous probabilities needed to render the decision contours and 3D surfaces." + ] + }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 25, "id": "9db6e30b", "metadata": {}, "outputs": [], @@ -5762,7 +5744,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 26, "id": "b7684723", "metadata": {}, "outputs": [ @@ -5790,19 +5772,31 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "d1a9a2c1", + "metadata": {}, + "source": [ + "This plot visualizes the continuous probability space generated by the trained bivariate model:\n", + "\n", + "* **Sample Distribution:** Blue squares represent non-virginica samples ($y=0$), and green triangles represent *Iris virginica* ($y=1$) mapped across Sepal Length and Sepal Width.\n", + "* **Probability Contours (`plt.contour`):** The labeled contour lines map specific probability thresholds. They show how the model's prediction confidence transitions across the 2D space.\n", + "* **Decision Boundary:** The contour line labeled **0.5** marks the exact geometric threshold. Any sample falling past this line is classified as *Iris virginica*, capturing the spatial trade-off between both sepal measurements." + ] + }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 27, "id": "0b4ce6e6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 71, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, @@ -5823,17 +5817,43 @@ "ax.scatter(iris.data[:,0:1], iris.data[:,1:2], y, 'or')" ] }, + { + "cell_type": "markdown", + "id": "9d50e3ae", + "metadata": {}, + "source": [ + "This cell projects the bivariate logistic regression model into a 3D coordinate space to visualize the complete probability landscape:\n", + "\n", + "* **Axis Dimensions:** The horizontal axes represent **Sepal Length** ($x_1$) and **Sepal Width** ($x_2$), while the vertical axis ($Z$) tracks the continuous model probability $\\sigma(z) \\in [0, 1]$.\n", + "* **Probability Surface (`plot_surface`):** The Sigmoid function is rendered as a 3D sheet using the `jet` colormap. It displays the non-linear S-curve transition dynamically stretched across the two-dimensional feature plane.\n", + "* **True Labels Spatial Scatter (`ax.scatter`):** The red markers plot the actual samples at their exact spatial coordinates and true binary height ($z = 1$ for *Iris virginica*, $z = 0$ for others). This highlights how the optimized surface splits the space to fit the data points." + ] + }, { "cell_type": "markdown", "id": "441defe5", "metadata": {}, "source": [ - "# Multiple features and muticlass classifier" + "### Modelo 5: Multiple features and muticlass classifier" + ] + }, + { + "cell_type": "markdown", + "id": "a55f12fd", + "metadata": {}, + "source": [ + "#### Multi-Feature & Multi-Class Model Training\n", + "\n", + "This cell configures and trains the final baseline model to handle all three species simultaneously within a two-dimensional feature space:\n", + "\n", + "* **Bivariate Inputs (`X`):** Slices index `[:, 0:2]` to utilize both **Sepal Length** and **Sepal Width** as the predictor variables.\n", + "* **Multi-Class Target (`y`):** Retains the original multi-class target labels (`0`, `1`, `2`) without applying binarization, expanding the task from a single boundary to a three-class decision space.\n", + "* **`LogisticRegression(C=100, solver='lbfgs')`:** Trains a multinomial classifier. The algorithm optimizes a distinct set of weights and biases for each target category, preparing the model to partition the 2D plane into three distinct classification zones." ] }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 28, "id": "5112f48b", "metadata": {}, "outputs": [ @@ -7097,7 +7117,7 @@ "LogisticRegression(C=100, random_state=22)" ] }, - "execution_count": 81, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -7113,9 +7133,23 @@ "lrmc.fit(X,y)" ] }, + { + "cell_type": "markdown", + "id": "27f0224a", + "metadata": {}, + "source": [ + "#### Multi-Class Grid Generation and Probability Evaluation\n", + "\n", + "This cell sets up the coordinate testing matrix to evaluate the multi-class prediction behavior across the entire sepal feature space:\n", + "\n", + "* **`np.meshgrid`**: Constructs a dense $100 \\times 100$ coordinate grid bounding the Sepal Length (3–8 cm) and Sepal Width (0–6 cm) ranges.\n", + "* **`Xnew` (`np.c_`)**: Flattens and pairs the grid elements into a matrix of 10,000 distinct 2D spatial coordinates.\n", + "* **`lrmc.predict_proba(Xnew)`**: Computes a three-column probability matrix for each point. This mapping determines the exact multi-class boundaries by evaluating the localized likelihood for *Setosa*, *Versicolor*, and *Virginica* simultaneously." + ] + }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 29, "id": "319922f0", "metadata": {}, "outputs": [], @@ -7130,7 +7164,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 30, "id": "cfd77334", "metadata": {}, "outputs": [ @@ -7159,9 +7193,21 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "c434bf4d", + "metadata": {}, + "source": [ + "This plot maps the continuous probability distribution of the middle class within the multi-class decision space:\n", + "\n", + "* **Three-Class Distribution:** Displays all species simultaneously using distinct markers: blue dots for *Setosa* ($y=0$), green pluses for *Versicolor* ($y=1$), and magenta stars for *Virginica* ($y=2$).\n", + "* **Target Class Extraction (`yPred[:, 1]`):** Slicing the second column of the probability matrix isolates and tracks the specific localized likelihood of a sample being **Iris versicolor**.\n", + "* **Localized Probability Ridge:** Unlike the linear boundaries seen in binary classification, the multinomial model creates a bounded peak or \"ridge\" to isolate the middle class. The highest contour line (**0.90**) tightly encapsulates the core *Versicolor* cluster, dropping off systematically as the features move toward *Setosa* (left) or *Virginica* (right) territories." + ] + }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 31, "id": "07e23e02", "metadata": {}, "outputs": [ @@ -7191,15 +7237,40 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "319cce3c", + "metadata": {}, + "source": [ + "This plot visualizes the ultimate classification boundaries by partitioning the entire 2D feature space into hard decision zones:\n", + "\n", + "* **Hard Class Assignment (`lrmc.predict`):** Converts continuous probabilities into discrete class verdicts (`0`, `1`, or `2`) by applying an *argmax* function (selecting the class with the highest probability for each point).\n", + "* **Filled Decision Regions (`plt.contourf`):** Shades the coordinate plane into three distinct, solid zones using the `jet` colormap:\n", + " * **Blue Region:** Absolute classification space for *Iris setosa*.\n", + " * **Green Region:** Absolute classification space for *Iris versicolor*.\n", + " * **Red/Orange Region:** Absolute classification space for *Iris virginica*.\n", + "* **Boundary Analysis:** The sharp geometric intersections between the colored blocks define the definitive decision thresholds. This layout explicitly reveals how the linear multi-class model manages the regional trade-offs and handles the spatial overlap between the *Versicolor* and *Virginica* samples." + ] + }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 35, "id": "fbac3068", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" ] @@ -7211,7 +7282,19 @@ "source": [ "fig, ax =plt.subplots(subplot_kw={\"projection\": \"3d\"})\n", "surf = ax.plot_surface(x0,x1,zz, cmap='jet')\n", - "#ax.scatter(iris.data[:,0:1], iris.data[:,1:2], y, 'or')" + "ax.scatter(iris.data[:,0:1], iris.data[:,1:2], y, 'or')" + ] + }, + { + "cell_type": "markdown", + "id": "d39857de", + "metadata": {}, + "source": [ + "This cell integrates the actual dataset samples into the 3D hard decision space to visually evaluate the multi-class model's accuracy:\n", + "\n", + "* **Discrete Vertical Alignment ($Z$):** Both the staircase surface and the scatter markers use the integer multi-class taxonomy ($0$ for *Setosa*, $1$ for *Versicolor*, and $2$ for *Virginica*) instead of continuous probabilities.\n", + "* **Stepped Surface (`plot_surface`):** Represents the geometric boundaries computed by the model. Each level dictates the categorical verdict zone based on the combination of sepal features.\n", + "* **True Labels Scatter (`ax.scatter`):** Plots the real flower samples at their actual feature coordinates and true species height. This allows immediate visual verification of performance: samples resting on their matching colored step are correctly classified, while those caught on the wrong tier highlight the exact instances of classification error caused by spatial overlap." ] } ],